Articles | Volume 11, issue 8
https://doi.org/10.5194/gmd-11-3235-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-3235-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Isoprene-derived secondary organic aerosol in the global aerosol–chemistry–climate model ECHAM6.3.0–HAM2.3–MOZ1.0
Scarlet Stadtler
Institut für Energie- und Klimaforschung, IEK-8, Forschungszentrum Jülich, Jülich, Germany
Thomas Kühn
Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland
Department of Applied Physics, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland
Sabine Schröder
Institut für Energie- und Klimaforschung, IEK-8, Forschungszentrum Jülich, Jülich, Germany
Domenico Taraborrelli
Institut für Energie- und Klimaforschung, IEK-8, Forschungszentrum Jülich, Jülich, Germany
Martin G. Schultz
Institut für Energie- und Klimaforschung, IEK-8, Forschungszentrum Jülich, Jülich, Germany
now at: Jülich Supercomputing Centre, JSC, Forschungszentrum Jülich, Jülich, Germany
Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland
Related authors
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler
Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, https://doi.org/10.5194/essd-13-3013-2021, 2021
Short summary
Short summary
With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata from more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real-world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.
Harri Kokkola, Thomas Kühn, Anton Laakso, Tommi Bergman, Kari E. J. Lehtinen, Tero Mielonen, Antti Arola, Scarlet Stadtler, Hannele Korhonen, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Ina Tegen, Colombe Siegenthaler-Le Drian, Martin G. Schultz, Isabelle Bey, Philip Stier, Nikos Daskalakis, Colette L. Heald, and Sami Romakkaniemi
Geosci. Model Dev., 11, 3833–3863, https://doi.org/10.5194/gmd-11-3833-2018, https://doi.org/10.5194/gmd-11-3833-2018, 2018
Short summary
Short summary
In this paper we present a global aerosol–chemistry–climate model with the focus on its representation for atmospheric aerosol particles. In the model, aerosols are simulated using the aerosol module SALSA2.0, which in this paper is compared to satellite, ground, and aircraft-based observations of the properties of atmospheric aerosol. Based on this study, the model simulated aerosol properties compare well with the observations.
Martin G. Schultz, Scarlet Stadtler, Sabine Schröder, Domenico Taraborrelli, Bruno Franco, Jonathan Krefting, Alexandra Henrot, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Colombe Siegenthaler-Le Drian, Sebastian Wahl, Harri Kokkola, Thomas Kühn, Sebastian Rast, Hauke Schmidt, Philip Stier, Doug Kinnison, Geoffrey S. Tyndall, John J. Orlando, and Catherine Wespes
Geosci. Model Dev., 11, 1695–1723, https://doi.org/10.5194/gmd-11-1695-2018, https://doi.org/10.5194/gmd-11-1695-2018, 2018
Short summary
Short summary
The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols. It thus allows for detailed investigations of chemical processes in the climate system. Evaluation of the model with various observational data yields good results, but the model has a tendency to produce too much OH in the tropics. This highlights the important interplay between atmospheric chemistry and dynamics.
Scarlet Stadtler, David Simpson, Sabine Schröder, Domenico Taraborrelli, Andreas Bott, and Martin Schultz
Atmos. Chem. Phys., 18, 3147–3171, https://doi.org/10.5194/acp-18-3147-2018, https://doi.org/10.5194/acp-18-3147-2018, 2018
Muhammed Irfan, Thomas Kühn, Taina Yli-Juuti, Anton Laakso, Eemeli Holopainen, Douglas R. Worsnop, Annele Virtanen, and Harri Kokkola
EGUsphere, https://doi.org/10.5194/egusphere-2023-2768, https://doi.org/10.5194/egusphere-2023-2768, 2023
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The study underscores the influence of semi-volatile organic compounds on the formation of secondary organic aerosol (SOA). Our findings demonstrate their considerable impact on climate, emphasizing the necessity to improve their representation in large-scale climate models. This research provides a nuanced perspective on the challenges in modelling, emphasizing the significance of semi-volatile compounds and their volatility distribution in predicting global climate patterns.
Anton Laakso, Daniele Visioni, Ulrike Niemeier, Simone Tilmes, and Harri Kokkola
EGUsphere, https://doi.org/10.5194/egusphere-2023-2520, https://doi.org/10.5194/egusphere-2023-2520, 2023
Short summary
Short summary
This study is the second in a two-part series in which we explore the dependency of the impacts of stratospheric sulfur injections on both the model employed and the strategy of injection utilized. The study uncovers uncertainties associated with these techniques to cool climate, highlighting how the simulated climate impacts are dependent on both the selected model and the magnitude of the injections. We also show that estimating precipitation impacts of aerosol injection is a complex task.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2587, https://doi.org/10.5194/egusphere-2023-2587, 2023
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation we find that MESSy’s bias in modelling routinely observed inorganic aerosol mass concentrations is reduced. Furthermore, the representation of fine aerosol pH is particularly improved in the marine boundary layer.
Alejandro Baró Pérez, Michael S. Diamond, Frida A.-M. Bender, Abhay Devasthale, Matthias Schwarz, Julien Savre, Juha Tonttila, Harri Kokkola, Hyunho Lee, David Painemal, and Annica M. L. Ekman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2070, https://doi.org/10.5194/egusphere-2023-2070, 2023
Short summary
Short summary
We use a numerical model to study interactions between humid light-absorbing aerosol plumes, clouds, and radiation over the Southeast Atlantic. We find that the warming produced by the aerosols reduces cloud cover, especially in highly polluted situations. Aerosol impacts on drizzle play a minor role. However, aerosol effects on cloud reflectivity and moisture-induced changes in cloud cover dominate the climatic response and lead to an overall cooling by the biomass-burning plumes.
Tamara Emmerichs, Yen-Sen Lu, and Domenico Taraborrelli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2306, https://doi.org/10.5194/egusphere-2023-2306, 2023
Short summary
Short summary
We assess the representation of the plant' response to surface water in a global atmospheric chemistry model. This sensitivity is crucial for the return of precipitation back to the atmosphere and thus significantly impacts the representation of weather as well as air quality. The newly implemented response function reduces this process towards a better comparison with satellite observations. This yields a higher intensity of unusual warm periods and a higher production of air pollutants.
Marc von Hobe, Domenico Taraborrelli, Sascha Alber, Birger Bohn, Hans-Peter Dorn, Hendrik Fuchs, Yun Li, Chenxi Qiu, Franz Rohrer, Roberto Sommariva, Fred Stroh, Zhaofeng Tan, Sergej Wedel, and Anna Novelli
Atmos. Chem. Phys., 23, 10609–10623, https://doi.org/10.5194/acp-23-10609-2023, https://doi.org/10.5194/acp-23-10609-2023, 2023
Short summary
Short summary
The trace gas carbonyl sulfide (OCS) transports sulfur from the troposphere to the stratosphere, where sulfate aerosols are formed that influence climate and stratospheric chemistry. An uncertain OCS source in the troposphere is chemical production form dimethyl sulfide (DMS), a gas released in large quantities from the oceans. We carried out experiments in a large atmospheric simulation chamber to further elucidate the chemical mechanism of OCS production from DMS.
Kalle Nordling, Jukka-Pekka Keskinen, Sami Romakkaniemi, Harri Kokkola, Petri Räisänen, Antti Lipponen, Antti-Ilari Partanen, Jaakko Ahola, Juha Tonttila, Muzaffer Ege Alper, Hannele Korhonen, and Tomi Raatikainen
EGUsphere, https://doi.org/10.5194/egusphere-2023-912, https://doi.org/10.5194/egusphere-2023-912, 2023
Short summary
Short summary
This paper shows how use machine learning methods to for model of small scale atmospherics physics model (large eddy simulation) which cover physics to the 100 m scale and implement that model to global large scale model. Our results shows that the global model is stable and it provides meaningful results. This way we can include physic based presentation of sub-grid (physics which happens in 100 m scale) physics to the global model which resolution is in 100 km scale.
Felix Wieser, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-102, https://doi.org/10.5194/gmd-2023-102, 2023
Revised manuscript under review for GMD
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA was expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phase was estimated and included in the preexisting scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous phase degradation reactions and box model limitations.
Meghna Soni, Rolf Sander, Lokesh K. Sahu, Domenico Taraborrelli, Pengfei Liu, Ankit Patel, Imran A. Girach, Andrea Pozzer, Sachin S. Gunthe, and Narendra Ojha
EGUsphere, https://doi.org/10.5194/egusphere-2023-652, https://doi.org/10.5194/egusphere-2023-652, 2023
Short summary
Short summary
The study presents the implementation of comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA. Simulations for contrasting urban environments of Asia and Europe highlight the significant impacts of chlorine on atmospheric oxidation capacity and composition. Chemical processes governing the production and loss of chlorine-containing species have been discussed. The updated chemical mechanism will be useful to interpret field measurements and for future air quality studies.
Tuuli Miinalainen, Harri Kokkola, Antti Lipponen, Antti-Pekka Hyvärinen, Vijay Kumar Soni, Kari E. J. Lehtinen, and Thomas Kühn
Atmos. Chem. Phys., 23, 3471–3491, https://doi.org/10.5194/acp-23-3471-2023, https://doi.org/10.5194/acp-23-3471-2023, 2023
Short summary
Short summary
We simulated the effects of aerosol emission mitigation on both global and regional radiative forcing and city-level air quality with a global-scale climate model. We used a machine learning downscaling approach to bias-correct the PM2.5 values obtained from the global model for the Indian megacity New Delhi. Our results indicate that aerosol mitigation could result in both improved air quality and less radiative heating for India.
Flora Kluge, Tilman Hüneke, Christophe Lerot, Simon Rosanka, Meike K. Rotermund, Domenico Taraborrelli, Benjamin Weyland, and Klaus Pfeilsticker
Atmos. Chem. Phys., 23, 1369–1401, https://doi.org/10.5194/acp-23-1369-2023, https://doi.org/10.5194/acp-23-1369-2023, 2023
Short summary
Short summary
Using airborne glyoxal concentration and vertical column density measurements, vertical profiles are inferred for eight global regions in aged biomass burning plumes and the tropical marine boundary layer. Using TROPOMI observations, an analysis of space- and airborne measurements is performed. A comparison to EMAC simulations shows a general glyoxal underprediction, which points to various missing sources and precursors from anthropogenic activities, biomass burning, and the sea surface.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
Short summary
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
Short summary
Short summary
We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
Short summary
Short summary
The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
Sini Isokääntä, Paul Kim, Santtu Mikkonen, Thomas Kühn, Harri Kokkola, Taina Yli-Juuti, Liine Heikkinen, Krista Luoma, Tuukka Petäjä, Zak Kipling, Daniel Partridge, and Annele Virtanen
Atmos. Chem. Phys., 22, 11823–11843, https://doi.org/10.5194/acp-22-11823-2022, https://doi.org/10.5194/acp-22-11823-2022, 2022
Short summary
Short summary
This research employs air mass history analysis and observations to study how clouds and precipitation affect atmospheric aerosols during transport to a boreal forest site. The mass concentrations of studied chemical species showed exponential decrease as a function of accumulated rain along the air mass route. Our analysis revealed in-cloud sulfate formation, while no major changes in organic mass were seen. Most of the in-cloud-formed sulfate could be assigned to particle sizes above 200 nm.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
Short summary
Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Marje Prank, Juha Tonttila, Jaakko Ahola, Harri Kokkola, Thomas Kühn, Sami Romakkaniemi, and Tomi Raatikainen
Atmos. Chem. Phys., 22, 10971–10992, https://doi.org/10.5194/acp-22-10971-2022, https://doi.org/10.5194/acp-22-10971-2022, 2022
Short summary
Short summary
Aerosols and clouds persist as the dominant sources of uncertainty in climate projections. In this modelling study, we investigate the role of marine aerosols in influencing the lifetime of low-level clouds. Our high resolution simulations show that sea spray can both extend and shorten the lifetime of the cloud layer depending on the model setup. The impact of the primary marine organics is relatively limited while secondary aerosol from monoterpenes can have larger impact.
Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
Short summary
Short summary
The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Short summary
Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
Short summary
Short summary
Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537, https://doi.org/10.5194/acp-22-4523-2022, https://doi.org/10.5194/acp-22-4523-2022, 2022
Short summary
Short summary
Clouds are important for the climate, and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools and water vapour condenses on small particles that can be natural or of anthropogenic origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and which properties are vital to updrafts.
Andrea Pozzer, Simon F. Reifenberg, Vinod Kumar, Bruno Franco, Matthias Kohl, Domenico Taraborrelli, Sergey Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev., 15, 2673–2710, https://doi.org/10.5194/gmd-15-2673-2022, https://doi.org/10.5194/gmd-15-2673-2022, 2022
Short summary
Short summary
A newly developed setup of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is evaluated here. A comprehensive organic degradation mechanism is used and coupled with a volatility base model.
The results show that the model reproduces most of the tracers and aerosols satisfactorily but shows discrepancies for oxygenated organic gases. It is also shown that this model configuration can be used for further research in atmospheric chemistry.
Tomi Raatikainen, Marje Prank, Jaakko Ahola, Harri Kokkola, Juha Tonttila, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 3763–3778, https://doi.org/10.5194/acp-22-3763-2022, https://doi.org/10.5194/acp-22-3763-2022, 2022
Short summary
Short summary
Mineral dust or similar ice-nucleating particles (INPs) are needed to initiate cloud droplet freezing at temperatures common in shallow clouds. In this work we examine how INPs that are released from the sea surface impact marine clouds. Our high-resolution simulations show that turbulent updraughts carry these particles effectively up to the clouds, where they initiate cloud droplet freezing. Sea surface INP emissions become more important with decreasing background dust INP concentrations.
Anton Laakso, Ulrike Niemeier, Daniele Visioni, Simone Tilmes, and Harri Kokkola
Atmos. Chem. Phys., 22, 93–118, https://doi.org/10.5194/acp-22-93-2022, https://doi.org/10.5194/acp-22-93-2022, 2022
Short summary
Short summary
The use of different spatio-temporal sulfur injection strategies with different magnitudes to create an artificial reflective aerosol layer to cool the climate is studied using sectional and modal aerosol schemes in a climate model. There are significant differences in the results depending on the aerosol microphysical module used. Different spatio-temporal injection strategies have a significant impact on the magnitude and zonal distribution of radiative forcing and atmospheric dynamics.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
Short summary
Short summary
Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Simon Rosanka, Bruno Franco, Lieven Clarisse, Pierre-François Coheur, Andrea Pozzer, Andreas Wahner, and Domenico Taraborrelli
Atmos. Chem. Phys., 21, 11257–11288, https://doi.org/10.5194/acp-21-11257-2021, https://doi.org/10.5194/acp-21-11257-2021, 2021
Short summary
Short summary
The strong El Niño in 2015 led to a particular dry season in Indonesia and favoured severe peatland fires. The smouldering conditions of these fires and the high carbon content of peat resulted in high volatile organic compound (VOC) emissions. By using a comprehensive atmospheric model, we show that these emissions have a significant impact on the tropospheric composition and oxidation capacity. These emissions are transported into to the lower stratosphere, resulting in a depletion of ozone.
Tamara Emmerichs, Bruno Franco, Catherine Wespes, Vinod Kumar, Andrea Pozzer, Simon Rosanka, and Domenico Taraborrelli
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-584, https://doi.org/10.5194/acp-2021-584, 2021
Revised manuscript not accepted
Short summary
Short summary
Near-surface ozone is a harmful air pollutant and it is strongly affected by radical reactions and surface-atmosphere exchanges which in turn are modulated, directly and indirectly, by weather. Understanding the impact of weather on ozone, and air quality, is thus important also in view of weather extremes. The inclusion of additional ozone-weather links in the global model yields a 2-fold reduction of the ozone bias towards satellite observations.
Simon Rosanka, Rolf Sander, Andreas Wahner, and Domenico Taraborrelli
Geosci. Model Dev., 14, 4103–4115, https://doi.org/10.5194/gmd-14-4103-2021, https://doi.org/10.5194/gmd-14-4103-2021, 2021
Short summary
Short summary
The Jülich Aqueous-phase Mechanism of Organic Chemistry (JAMOC) is developed and implemented into the Module Efficiently Calculating the Chemistry of the Atmosphere (MECCA). JAMOC is an explicit in-cloud oxidation scheme for oxygenated volatile organic compounds (OVOCs), which is suitable for global model applications. Within a box-model study, we show that JAMOC yields reduced gas-phase concentrations of most OVOCs and oxidants, except for nitrogen oxides.
Simon Rosanka, Rolf Sander, Bruno Franco, Catherine Wespes, Andreas Wahner, and Domenico Taraborrelli
Atmos. Chem. Phys., 21, 9909–9930, https://doi.org/10.5194/acp-21-9909-2021, https://doi.org/10.5194/acp-21-9909-2021, 2021
Short summary
Short summary
In-cloud destruction of ozone depends on hydroperoxyl radicals in cloud droplets, where they are produced by oxygenated volatile organic compound (OVOC) oxygenation. Only rudimentary representations of these processes, if any, are currently available in global atmospheric models. By using a comprehensive atmospheric model that includes a complex in-cloud OVOC oxidation scheme, we show that atmospheric oxidants are reduced and models ignoring this process will underpredict clouds as ozone sinks.
Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler
Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, https://doi.org/10.5194/essd-13-3013-2021, 2021
Short summary
Short summary
With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata from more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real-world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021, https://doi.org/10.5194/gmd-14-1553-2021, 2021
Short summary
Short summary
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
Domenico Taraborrelli, David Cabrera-Perez, Sara Bacer, Sergey Gromov, Jos Lelieveld, Rolf Sander, and Andrea Pozzer
Atmos. Chem. Phys., 21, 2615–2636, https://doi.org/10.5194/acp-21-2615-2021, https://doi.org/10.5194/acp-21-2615-2021, 2021
Short summary
Short summary
Atmospheric pollutants from anthropogenic activities and biomass burning are usually regarded as ozone precursors. Monocyclic aromatics are no exception. Calculations with a comprehensive atmospheric model are consistent with this view but only for air masses close to pollution source regions. However, the same model predicts that aromatics, when transported to remote areas, may effectively destroy ozone. This loss of tropospheric ozone rivals the one attributed to bromine.
Antti Ruuskanen, Sami Romakkaniemi, Harri Kokkola, Antti Arola, Santtu Mikkonen, Harri Portin, Annele Virtanen, Kari E. J. Lehtinen, Mika Komppula, and Ari Leskinen
Atmos. Chem. Phys., 21, 1683–1695, https://doi.org/10.5194/acp-21-1683-2021, https://doi.org/10.5194/acp-21-1683-2021, 2021
Short summary
Short summary
The study focuses mainly on cloud-scavenging efficiency of absorbing particulate matter (mainly black carbon) but additionally covers cloud-scavenging efficiency of scattering particles and statistics of cloud condensation nuclei. The main findings give insight into how black carbon is distributed in different particle sizes and the sensitivity to cloud scavenged. The main findings are useful for large-scale modelling for evaluating cloud scavenging.
Juha Tonttila, Ali Afzalifar, Harri Kokkola, Tomi Raatikainen, Hannele Korhonen, and Sami Romakkaniemi
Atmos. Chem. Phys., 21, 1035–1048, https://doi.org/10.5194/acp-21-1035-2021, https://doi.org/10.5194/acp-21-1035-2021, 2021
Short summary
Short summary
The focus of this study is on rain enhancement by deliberate injection of small particles into clouds (
cloud seeding). The particles, usually released from an aircraft, are expected to enhance cloud droplet growth, but its practical feasibility is somewhat uncertain. To improve upon this, we simulate the seeding effects with a numerical model. The model reproduces the main features seen in field observations, with a strong sensitivity to the total mass of the injected particle material.
Tamara Emmerichs, Astrid Kerkweg, Huug Ouwersloot, Silvano Fares, Ivan Mammarella, and Domenico Taraborrelli
Geosci. Model Dev., 14, 495–519, https://doi.org/10.5194/gmd-14-495-2021, https://doi.org/10.5194/gmd-14-495-2021, 2021
Short summary
Short summary
Dry deposition to vegetation is a major sink of ground-level ozone. Its parameterization in atmospheric chemistry models represents a significant source of uncertainty for global tropospheric ozone. We extended the current model parameterization with a relevant pathway and important meteorological adjustment factors. The comparison with measurements shows that this enables a more realistic model representation of ozone dry deposition velocity. Globally, annual dry deposition loss increases.
Jonas Gliß, Augustin Mortier, Michael Schulz, Elisabeth Andrews, Yves Balkanski, Susanne E. Bauer, Anna M. K. Benedictow, Huisheng Bian, Ramiro Checa-Garcia, Mian Chin, Paul Ginoux, Jan J. Griesfeller, Andreas Heckel, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Paolo Laj, Philippe Le Sager, Marianne Tronstad Lund, Cathrine Lund Myhre, Hitoshi Matsui, Gunnar Myhre, David Neubauer, Twan van Noije, Peter North, Dirk J. L. Olivié, Samuel Rémy, Larisa Sogacheva, Toshihiko Takemura, Kostas Tsigaridis, and Svetlana G. Tsyro
Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, https://doi.org/10.5194/acp-21-87-2021, 2021
Short summary
Short summary
Simulated aerosol optical properties as well as the aerosol life cycle are investigated for 14 global models participating in the AeroCom initiative. Considerable diversity is found in the simulated aerosol species emissions and lifetimes, also resulting in a large diversity in the simulated aerosol mass, composition, and optical properties. A comparison with observations suggests that, on average, current models underestimate the direct effect of aerosol on the atmosphere radiation budget.
Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz
Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, https://doi.org/10.5194/gmd-14-1-2021, 2021
Short summary
Short summary
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.
Eemeli Holopainen, Harri Kokkola, Anton Laakso, and Thomas Kühn
Geosci. Model Dev., 13, 6215–6235, https://doi.org/10.5194/gmd-13-6215-2020, https://doi.org/10.5194/gmd-13-6215-2020, 2020
Short summary
Short summary
This paper introduces an in-cloud wet deposition scheme for liquid and ice phase clouds for global aerosol–climate models. With the default setup, our wet deposition scheme behaves spuriously and better representation can be achieved with this scheme when black carbon is mixed with soluble compounds at emission time. This work is done as many of the global models fail to reproduce the transport of black carbon to the Arctic, which may be due to the poor representation of wet removal in models.
Jessica Slater, Juha Tonttila, Gordon McFiggans, Paul Connolly, Sami Romakkaniemi, Thomas Kühn, and Hugh Coe
Atmos. Chem. Phys., 20, 11893–11906, https://doi.org/10.5194/acp-20-11893-2020, https://doi.org/10.5194/acp-20-11893-2020, 2020
Short summary
Short summary
The feedback effect between aerosol particles, radiation and meteorology reduces turbulent motion and results in increased surface aerosol concentrations during Beijing haze. Observational analysis and regional modelling studies have examined the feedback effect but these studies are limited. In this work, we set up a high-resolution model for the Beijing environment to examine the sensitivity of the aerosol feedback effect to initial meteorological conditions and aerosol loading.
Jaakko Ahola, Hannele Korhonen, Juha Tonttila, Sami Romakkaniemi, Harri Kokkola, and Tomi Raatikainen
Atmos. Chem. Phys., 20, 11639–11654, https://doi.org/10.5194/acp-20-11639-2020, https://doi.org/10.5194/acp-20-11639-2020, 2020
Short summary
Short summary
In this study, we present an improved cloud model that reproduces the behaviour of mixed-phase clouds containing liquid droplets and ice crystals in more detail than before. This model is a convenient computational tool that enables the study of phenomena that cannot fit into a laboratory. These clouds have a significant role in climate, but they are not yet properly understood. Here, we show the advantages of the new model in a case study focusing on Arctic mixed-phase clouds.
Innocent Kudzotsa, Harri Kokkola, Juha Tonttila, Tomi Raatikainen, and Sami Romakkaniemi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-851, https://doi.org/10.5194/acp-2020-851, 2020
Publication in ACP not foreseen
María A. Burgos, Elisabeth Andrews, Gloria Titos, Angela Benedetti, Huisheng Bian, Virginie Buchard, Gabriele Curci, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Anton Laakso, Julie Letertre-Danczak, Marianne T. Lund, Hitoshi Matsui, Gunnar Myhre, Cynthia Randles, Michael Schulz, Twan van Noije, Kai Zhang, Lucas Alados-Arboledas, Urs Baltensperger, Anne Jefferson, James Sherman, Junying Sun, Ernest Weingartner, and Paul Zieger
Atmos. Chem. Phys., 20, 10231–10258, https://doi.org/10.5194/acp-20-10231-2020, https://doi.org/10.5194/acp-20-10231-2020, 2020
Short summary
Short summary
We investigate how well models represent the enhancement in scattering coefficients due to particle water uptake, and perform an evaluation of several implementation schemes used in ten Earth system models. Our results show the importance of the parameterization of hygroscopicity and model chemistry as drivers of some of the observed diversity amongst model estimates. The definition of dry conditions and the phenomena taking place in this relative humidity range also impact the model evaluation.
Martine G. de Vos, Wilco Hazeleger, Driss Bari, Jörg Behrens, Sofiane Bendoukha, Irene Garcia-Marti, Ronald van Haren, Sue Ellen Haupt, Rolf Hut, Fredrik Jansson, Andreas Mueller, Peter Neilley, Gijs van den Oord, Inti Pelupessy, Paolo Ruti, Martin G. Schultz, and Jeremy Walton
Geosci. Commun., 3, 191–201, https://doi.org/10.5194/gc-3-191-2020, https://doi.org/10.5194/gc-3-191-2020, 2020
Short summary
Short summary
At the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Open science offers manifold opportunities but goes beyond sharing code and data. Besides domain-specific technical challenges, we observed that the main challenges are non-technical and impact the system of science as a whole.
Simon Rosanka, Giang H. T. Vu, Hue M. T. Nguyen, Tien V. Pham, Umar Javed, Domenico Taraborrelli, and Luc Vereecken
Atmos. Chem. Phys., 20, 6671–6686, https://doi.org/10.5194/acp-20-6671-2020, https://doi.org/10.5194/acp-20-6671-2020, 2020
Short summary
Short summary
Isocyanic acid, HNCO, is a toxic chemical compound emitted to the atmosphere by biomass burning and by unwanted release in NOx mitigation systems in vehicles such as the AdBlue system. We have studied the loss processes of HNCO, finding that it is unreactive to most atmospheric oxidants and thus has a long chemical lifetime. The main removal is then by deposition on surfaces and transition to aqueous phase, such as clouds. The long lifetime also allows it to be transported to the stratosphere.
Thomas Kühn, Kaarle Kupiainen, Tuuli Miinalainen, Harri Kokkola, Ville-Veikko Paunu, Anton Laakso, Juha Tonttila, Rita Van Dingenen, Kati Kulovesi, Niko Karvosenoja, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 20, 5527–5546, https://doi.org/10.5194/acp-20-5527-2020, https://doi.org/10.5194/acp-20-5527-2020, 2020
Short summary
Short summary
We investigate the effects of black carbon (BC) mitigation on Arctic climate and human health, accounting for the concurrent reduction of other aerosol species. While BC is attributed a net warming effect on climate, most other aerosol species cool the planet. We find that the direct radiative effect of mitigating BC induces cooling, while aerosol–cloud effects offset this cooling and introduce large uncertainties. Furthermore, the reduced aerosol emissions reduce human mortality considerably.
Vincent Huijnen, Kazuyuki Miyazaki, Johannes Flemming, Antje Inness, Takashi Sekiya, and Martin G. Schultz
Geosci. Model Dev., 13, 1513–1544, https://doi.org/10.5194/gmd-13-1513-2020, https://doi.org/10.5194/gmd-13-1513-2020, 2020
Short summary
Short summary
We present the evaluation and intercomparison of global tropospheric ozone reanalyses that have been produced in recent years. Such reanalyses can be used to assess the current state and variability of tropospheric ozone.
The reanalyses show overall good agreements with independent ground and ozone-sonde observations for the diurnal, synoptical, seasonal, and interannual variabilities, with generally improved performances for the updated reanalyses.
Anna Novelli, Luc Vereecken, Birger Bohn, Hans-Peter Dorn, Georgios I. Gkatzelis, Andreas Hofzumahaus, Frank Holland, David Reimer, Franz Rohrer, Simon Rosanka, Domenico Taraborrelli, Ralf Tillmann, Robert Wegener, Zhujun Yu, Astrid Kiendler-Scharr, Andreas Wahner, and Hendrik Fuchs
Atmos. Chem. Phys., 20, 3333–3355, https://doi.org/10.5194/acp-20-3333-2020, https://doi.org/10.5194/acp-20-3333-2020, 2020
Short summary
Short summary
Experimental evidence from a simulation chamber study shows that the regeneration efficiency of the hydroxyl radical is maintained globally at values higher than 0.5 for a wide range of nitrogen oxide concentrations as a result of isomerizations of peroxy radicals originating from the OH oxidation of isoprene. The available models were tested, and suggestions on how to improve their ability to reproduce the measured radical and oxygenated volatile organic compound concentrations are provided.
Giulia Saponaro, Moa K. Sporre, David Neubauer, Harri Kokkola, Pekka Kolmonen, Larisa Sogacheva, Antti Arola, Gerrit de Leeuw, Inger H. H. Karset, Ari Laaksonen, and Ulrike Lohmann
Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, https://doi.org/10.5194/acp-20-1607-2020, 2020
Short summary
Short summary
The understanding of cloud processes is based on the quality of the representation of cloud properties. We compared cloud parameters from three models with satellite observations. We report on the performance of each data source, highlighting strengths and deficiencies, which should be considered when deriving the effect of aerosols on cloud properties.
Anina Gilgen, Stiig Wilkenskjeld, Jed O. Kaplan, Thomas Kühn, and Ulrike Lohmann
Clim. Past, 15, 1885–1911, https://doi.org/10.5194/cp-15-1885-2019, https://doi.org/10.5194/cp-15-1885-2019, 2019
Short summary
Short summary
Using the global aerosol–climate model ECHAM-HAM-SALSA, the effect of humans on European climate in the Roman Empire was quantified. Both land use and novel estimates of anthropogenic aerosol emissions were considered. We conducted simulations with fixed sea-surface temperatures to gain a first impression about the anthropogenic impact. While land use effects induced a regional warming for one of the reconstructions, aerosol emissions led to a cooling associated with aerosol–cloud interactions.
David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Philip Stier, Daniel G. Partridge, Ina Tegen, Isabelle Bey, Tanja Stanelle, Harri Kokkola, and Ulrike Lohmann
Geosci. Model Dev., 12, 3609–3639, https://doi.org/10.5194/gmd-12-3609-2019, https://doi.org/10.5194/gmd-12-3609-2019, 2019
Short summary
Short summary
The global aerosol–climate model ECHAM6.3–HAM2.3 as well as the previous model versions ECHAM5.5–HAM2.0 and ECHAM6.1–HAM2.2 are evaluated. The simulation of clouds has improved in ECHAM6.3–HAM2.3. This has an impact on effective radiative forcing due to aerosol–radiation and aerosol–cloud interactions and equilibrium climate sensitivity, which are weaker in ECHAM6.3–HAM2.3 than in the previous model versions.
Christoph Heinze, Veronika Eyring, Pierre Friedlingstein, Colin Jones, Yves Balkanski, William Collins, Thierry Fichefet, Shuang Gao, Alex Hall, Detelina Ivanova, Wolfgang Knorr, Reto Knutti, Alexander Löw, Michael Ponater, Martin G. Schultz, Michael Schulz, Pier Siebesma, Joao Teixeira, George Tselioudis, and Martin Vancoppenolle
Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, https://doi.org/10.5194/esd-10-379-2019, 2019
Short summary
Short summary
Earth system models for producing climate projections under given forcings include additional processes and feedbacks that traditional physical climate models do not consider. We present an overview of climate feedbacks for key Earth system components and discuss the evaluation of these feedbacks. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research.
Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann
Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019, https://doi.org/10.5194/gmd-12-1643-2019, 2019
Short summary
Short summary
We describe a new version of the aerosol–climate model ECHAM–HAM and show tests of the model performance by comparing different aspects of the aerosol distribution with different datasets. The updated version of HAM contains improved descriptions of aerosol processes, including updated emission fields and cloud processes. While there are regional deviations between the model and observations, the model performs well overall.
Mona Kurppa, Antti Hellsten, Pontus Roldin, Harri Kokkola, Juha Tonttila, Mikko Auvinen, Christoph Kent, Prashant Kumar, Björn Maronga, and Leena Järvi
Geosci. Model Dev., 12, 1403–1422, https://doi.org/10.5194/gmd-12-1403-2019, https://doi.org/10.5194/gmd-12-1403-2019, 2019
Short summary
Short summary
This paper describes the implementation of a sectional aerosol module, SALSA, into the PALM model system 6.0. The first evaluation study shows excellent agreements with measurements. Furthermore, we show that ignoring the dry deposition of aerosol particles can overestimate aerosol number concentrations by 20 %, whereas condensation and dissolutional growth increase the total aerosol mass by over 10 % in this specific urban environment.
Rolf Sander, Andreas Baumgaertner, David Cabrera-Perez, Franziska Frank, Sergey Gromov, Jens-Uwe Grooß, Hartwig Harder, Vincent Huijnen, Patrick Jöckel, Vlassis A. Karydis, Kyle E. Niemeyer, Andrea Pozzer, Hella Riede, Martin G. Schultz, Domenico Taraborrelli, and Sebastian Tauer
Geosci. Model Dev., 12, 1365–1385, https://doi.org/10.5194/gmd-12-1365-2019, https://doi.org/10.5194/gmd-12-1365-2019, 2019
Short summary
Short summary
We present the atmospheric chemistry box model CAABA/MECCA which
now includes a number of new features: skeletal mechanism
reduction, the MOM chemical mechanism for volatile organic
compounds, an option to include reactions from the Master
Chemical Mechanism (MCM) and other chemical mechanisms, updated
isotope tagging, improved and new photolysis modules, and the new
feature of coexisting multiple chemistry mechanisms.
CAABA/MECCA is a community model published under the GPL.
Kai-Lan Chang, Owen R. Cooper, J. Jason West, Marc L. Serre, Martin G. Schultz, Meiyun Lin, Virginie Marécal, Béatrice Josse, Makoto Deushi, Kengo Sudo, Junhua Liu, and Christoph A. Keller
Geosci. Model Dev., 12, 955–978, https://doi.org/10.5194/gmd-12-955-2019, https://doi.org/10.5194/gmd-12-955-2019, 2019
Short summary
Short summary
We developed a new method for combining surface ozone observations from thousands of monitoring sites worldwide with the output from multiple atmospheric chemistry models. The result is a global surface ozone distribution with greater accuracy than any single model can achieve. We focused on an ozone metric relevant to human mortality caused by long-term ozone exposure. Our method can be applied to studies that quantify the impacts of ozone on human health and mortality.
Arlene M. Fiore, Emily V. Fischer, George P. Milly, Shubha Pandey Deolal, Oliver Wild, Daniel A. Jaffe, Johannes Staehelin, Olivia E. Clifton, Dan Bergmann, William Collins, Frank Dentener, Ruth M. Doherty, Bryan N. Duncan, Bernd Fischer, Stefan Gilge, Peter G. Hess, Larry W. Horowitz, Alexandru Lupu, Ian A. MacKenzie, Rokjin Park, Ludwig Ries, Michael G. Sanderson, Martin G. Schultz, Drew T. Shindell, Martin Steinbacher, David S. Stevenson, Sophie Szopa, Christoph Zellweger, and Guang Zeng
Atmos. Chem. Phys., 18, 15345–15361, https://doi.org/10.5194/acp-18-15345-2018, https://doi.org/10.5194/acp-18-15345-2018, 2018
Short summary
Short summary
We demonstrate a proof-of-concept approach for applying northern midlatitude mountaintop peroxy acetyl nitrate (PAN) measurements and a multi-model ensemble during April to constrain the influence of continental-scale anthropogenic precursor emissions on PAN. Our findings imply a role for carefully coordinated multi-model ensembles in helping identify observations for discriminating among widely varying (and poorly constrained) model responses of atmospheric constituents to changes in emissions.
Harri Kokkola, Thomas Kühn, Anton Laakso, Tommi Bergman, Kari E. J. Lehtinen, Tero Mielonen, Antti Arola, Scarlet Stadtler, Hannele Korhonen, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Ina Tegen, Colombe Siegenthaler-Le Drian, Martin G. Schultz, Isabelle Bey, Philip Stier, Nikos Daskalakis, Colette L. Heald, and Sami Romakkaniemi
Geosci. Model Dev., 11, 3833–3863, https://doi.org/10.5194/gmd-11-3833-2018, https://doi.org/10.5194/gmd-11-3833-2018, 2018
Short summary
Short summary
In this paper we present a global aerosol–chemistry–climate model with the focus on its representation for atmospheric aerosol particles. In the model, aerosols are simulated using the aerosol module SALSA2.0, which in this paper is compared to satellite, ground, and aircraft-based observations of the properties of atmospheric aerosol. Based on this study, the model simulated aerosol properties compare well with the observations.
Chinmay Mallik, Laura Tomsche, Efstratios Bourtsoukidis, John N. Crowley, Bettina Derstroff, Horst Fischer, Sascha Hafermann, Imke Hüser, Umar Javed, Stephan Keßel, Jos Lelieveld, Monica Martinez, Hannah Meusel, Anna Novelli, Gavin J. Phillips, Andrea Pozzer, Andreas Reiffs, Rolf Sander, Domenico Taraborrelli, Carina Sauvage, Jan Schuladen, Hang Su, Jonathan Williams, and Hartwig Harder
Atmos. Chem. Phys., 18, 10825–10847, https://doi.org/10.5194/acp-18-10825-2018, https://doi.org/10.5194/acp-18-10825-2018, 2018
Short summary
Short summary
OH and HO2 control the transformation of air pollutants and O3 formation. Their implication for air quality over the climatically sensitive Mediterranean region was studied during a field campaign in Cyprus. Production of OH, HO2, and recycled OH was lower in aged marine air masses. Box model simulations of OH and HO2 agreed with measurements except at high terpene concentrations when model RO2 due to terpenes caused large HO2 loss. Autoxidation schemes for RO2 improved the agreement.
Ian Boutle, Jeremy Price, Innocent Kudzotsa, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 18, 7827–7840, https://doi.org/10.5194/acp-18-7827-2018, https://doi.org/10.5194/acp-18-7827-2018, 2018
Short summary
Short summary
Aerosol processes are a key mechanism in the development of fog. Poor representation of aerosol–fog interaction can result in large biases in fog forecasts, such as surface temperatures which are too high and fog which is too deep and long lived. A relatively simple representation of aerosol–fog interaction can actually lead to significant improvements in forecasting. Aerosol–fog interaction can have a large effect on the climate system but is poorly represented in climate models.
Martin G. Schultz, Scarlet Stadtler, Sabine Schröder, Domenico Taraborrelli, Bruno Franco, Jonathan Krefting, Alexandra Henrot, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Colombe Siegenthaler-Le Drian, Sebastian Wahl, Harri Kokkola, Thomas Kühn, Sebastian Rast, Hauke Schmidt, Philip Stier, Doug Kinnison, Geoffrey S. Tyndall, John J. Orlando, and Catherine Wespes
Geosci. Model Dev., 11, 1695–1723, https://doi.org/10.5194/gmd-11-1695-2018, https://doi.org/10.5194/gmd-11-1695-2018, 2018
Short summary
Short summary
The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols. It thus allows for detailed investigations of chemical processes in the climate system. Evaluation of the model with various observational data yields good results, but the model has a tendency to produce too much OH in the tropics. This highlights the important interplay between atmospheric chemistry and dynamics.
Scarlet Stadtler, David Simpson, Sabine Schröder, Domenico Taraborrelli, Andreas Bott, and Martin Schultz
Atmos. Chem. Phys., 18, 3147–3171, https://doi.org/10.5194/acp-18-3147-2018, https://doi.org/10.5194/acp-18-3147-2018, 2018
Lukas Pichelstorfer, Dominik Stolzenburg, John Ortega, Thomas Karl, Harri Kokkola, Anton Laakso, Kari E. J. Lehtinen, James N. Smith, Peter H. McMurry, and Paul M. Winkler
Atmos. Chem. Phys., 18, 1307–1323, https://doi.org/10.5194/acp-18-1307-2018, https://doi.org/10.5194/acp-18-1307-2018, 2018
Short summary
Short summary
Quantification of new particle formation as a source of atmospheric aerosol is clearly of importance for climate and health aspects. In our new study we developed two analysis methods that allow retrieval of nanoparticle growth dynamics at much higher precision than it was possible so far. Our results clearly demonstrate that growth rates show much more variation than is currently known and suggest that the Kelvin effect governs growth in the sub-10 nm size range.
David Cabrera-Perez, Domenico Taraborrelli, Jos Lelieveld, Thorsten Hoffmann, and Andrea Pozzer
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-928, https://doi.org/10.5194/acp-2017-928, 2017
Revised manuscript not accepted
Short summary
Short summary
Aromatic compounds are present in rural and urban atmospheres. The aim of this work is to disentangle the impacts of these compounds in different important atmospheric chemical species with the help of a numerical model. Aromatics have low impact OH, NOx and Ozone concentrations in the global scale (below 4 %). The impact however is larger in the regional scale (up to 10 %). The largest impact is in glyoxal and NO3 concentrations, with changes up to 10 % globally and 40 % regionally.
Florian Berkes, Patrick Neis, Martin G. Schultz, Ulrich Bundke, Susanne Rohs, Herman G. J. Smit, Andreas Wahner, Paul Konopka, Damien Boulanger, Philippe Nédélec, Valerie Thouret, and Andreas Petzold
Atmos. Chem. Phys., 17, 12495–12508, https://doi.org/10.5194/acp-17-12495-2017, https://doi.org/10.5194/acp-17-12495-2017, 2017
Short summary
Short summary
This study highlights the importance of independent global measurements with high and long-term accuracy to quantify long-term changes, especially in the UTLS region, and to help identify inconsistencies between different data sets of observations and models. Here we investigated temperature trends over different regions within a climate-sensitive area of the atmosphere and demonstrated the value of the IAGOS temperature observations as an anchor point for the evaluation of reanalyses.
Stephan Keßel, David Cabrera-Perez, Abraham Horowitz, Patrick R. Veres, Rolf Sander, Domenico Taraborrelli, Maria Tucceri, John N. Crowley, Andrea Pozzer, Christof Stönner, Luc Vereecken, Jos Lelieveld, and Jonathan Williams
Atmos. Chem. Phys., 17, 8789–8804, https://doi.org/10.5194/acp-17-8789-2017, https://doi.org/10.5194/acp-17-8789-2017, 2017
Short summary
Short summary
In this study we identify an often overlooked stable oxide of carbon, namely carbon suboxide (C3O2), in ambient air. We have made C3O2 and in the laboratory determined its absorption cross section data and the rate of reaction with two important atmospheric oxidants, OH and O3. By incorporating known sources and sinks in a global model we have generated a first global picture of the distribution of this species in the atmosphere.
Sami Romakkaniemi, Zubair Maalick, Antti Hellsten, Antti Ruuskanen, Olli Väisänen, Irshad Ahmad, Juha Tonttila, Santtu Mikkonen, Mika Komppula, and Thomas Kühn
Atmos. Chem. Phys., 17, 7955–7964, https://doi.org/10.5194/acp-17-7955-2017, https://doi.org/10.5194/acp-17-7955-2017, 2017
Short summary
Short summary
Surface topography affects aerosol–cloud interactions in boundary layer clouds. Local topography effects should be screened out from in situ observations before results can be generalised into a larger scale. Here we present modelling and observational results from a measurement station residing in a 75 m tower on top of a 150 m hill, and analyse how landscape affects the cloud formation, and which factors should be taken into account when aerosol effect on cloud droplet formation is studied.
Anton Laakso, Hannele Korhonen, Sami Romakkaniemi, and Harri Kokkola
Atmos. Chem. Phys., 17, 6957–6974, https://doi.org/10.5194/acp-17-6957-2017, https://doi.org/10.5194/acp-17-6957-2017, 2017
Short summary
Short summary
Based on simulations, equatorial stratospheric sulfur injections have shown to be an efficient strategy to counteract ongoing global warming. However, equatorial injections would result in relatively larger cooling in low latitudes than in high latitudes. This together with greenhouse-gas-induced warming would lead to cooling in the Equator and warming in the high latitudes. Results of this study show that a more optimal cooling effect is achieved by varying the injection area seasonally.
Antti Arola, Thomas F. Eck, Harri Kokkola, Mikko R. A. Pitkänen, and Sami Romakkaniemi
Atmos. Chem. Phys., 17, 5991–6001, https://doi.org/10.5194/acp-17-5991-2017, https://doi.org/10.5194/acp-17-5991-2017, 2017
Short summary
Short summary
One of the issues that hinder the measurement-based assessment of aerosol–cloud interactions by remote sensing methods is that typically aerosols and clouds cannot be measured simultaneously by passive remote sensing methods. AERONET includes the SDA product that provides the fine-mode AOD also in mixed cloud–aerosol observations. These measurements have not yet been fully exploited in studies of aerosol–cloud interactions. We applied SDA for this kind of analysis.
Alexandra-Jane Henrot, Tanja Stanelle, Sabine Schröder, Colombe Siegenthaler, Domenico Taraborrelli, and Martin G. Schultz
Geosci. Model Dev., 10, 903–926, https://doi.org/10.5194/gmd-10-903-2017, https://doi.org/10.5194/gmd-10-903-2017, 2017
Short summary
Short summary
This paper describes the basic results of the biogenic emission scheme, based on MEGAN, integrated into the ECHAM6-HAMMOZ chemistry climate model. Sensitivity to vegetation and climate-dependent parameters is also analysed. This version of the model is now suitable for many tropospheric investigations concerning the impact of biogenic volatile organic compound emissions on the ozone budget, secondary aerosol formation, and atmospheric chemistry.
Juha Tonttila, Zubair Maalick, Tomi Raatikainen, Harri Kokkola, Thomas Kühn, and Sami Romakkaniemi
Geosci. Model Dev., 10, 169–188, https://doi.org/10.5194/gmd-10-169-2017, https://doi.org/10.5194/gmd-10-169-2017, 2017
Short summary
Short summary
Novel techniques for modelling the aerosol–cloud interactions are implemented in a cloud-resolving model. The new methods improve the representation of the poorly constrained effects of cloud processing, precipitation and the wet removal of particles on the aerosol population and the associated feedbacks. The detailed representation of these processes yields more realistic simulation of the evolution of boundary layer clouds and fogs, as compared to results obtained using more simple methods.
Tero Mielonen, Anca Hienola, Thomas Kühn, Joonas Merikanto, Antti Lipponen, Tommi Bergman, Hannele Korhonen, Pekka Kolmonen, Larisa Sogacheva, Darren Ghent, Antti Arola, Gerrit de Leeuw, and Harri Kokkola
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-625, https://doi.org/10.5194/acp-2016-625, 2016
Revised manuscript not accepted
Short summary
Short summary
We studied the temperature dependence of AOD and its radiative effects over the southeastern US. We used spaceborne observations of AOD, LST and tropospheric NO2 with simulations of ECHAM-HAMMOZ. The level of AOD in this region is governed by anthropogenic emissions but the temperature dependency is most likely caused by BVOC emissions. According to the observations and simulations, the regional clear-sky DRE for biogenic aerosols is −0.43 ± 0.88 W/m2/K and −0.86 ± 0.06 W/m2/K, respectively.
Jani Huttunen, Harri Kokkola, Tero Mielonen, Mika Esa Juhani Mononen, Antti Lipponen, Juha Reunanen, Anders Vilhelm Lindfors, Santtu Mikkonen, Kari Erkki Juhani Lehtinen, Natalia Kouremeti, Alkiviadis Bais, Harri Niska, and Antti Arola
Atmos. Chem. Phys., 16, 8181–8191, https://doi.org/10.5194/acp-16-8181-2016, https://doi.org/10.5194/acp-16-8181-2016, 2016
Short summary
Short summary
For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
David Cabrera-Perez, Domenico Taraborrelli, Rolf Sander, and Andrea Pozzer
Atmos. Chem. Phys., 16, 6931–6947, https://doi.org/10.5194/acp-16-6931-2016, https://doi.org/10.5194/acp-16-6931-2016, 2016
Short summary
Short summary
The global atmospheric budget and distribution of monocyclic aromatic compounds is estimated, using an atmospheric chemistry general circulation model. Simulation results are evaluated with observations with the goal of understanding emission, production and removal of these compounds. Anthropogenic and biomass burning are the main sources of aromatic compounds to the atmosphere. The main sink is photochemical decomposition and in lesser importance dry deposition.
Olga Lyapina, Martin G. Schultz, and Andreas Hense
Atmos. Chem. Phys., 16, 6863–6881, https://doi.org/10.5194/acp-16-6863-2016, https://doi.org/10.5194/acp-16-6863-2016, 2016
Short summary
Short summary
This study applies numerical clustering for the classification of about 1500 ozone data sets in Europe. We show the usefulness of cluster analysis (CA) for the quantitative evaluation of a global model: pre-selection of stations and validation of a global model in a phase-space produce clearer and more interpretable results. CA can be easily updated for new stations, different length of data, and other type of input properties, as well as other type of data (for example, meteorological).
N. I. Kristiansen, A. Stohl, D. J. L. Olivié, B. Croft, O. A. Søvde, H. Klein, T. Christoudias, D. Kunkel, S. J. Leadbetter, Y. H. Lee, K. Zhang, K. Tsigaridis, T. Bergman, N. Evangeliou, H. Wang, P.-L. Ma, R. C. Easter, P. J. Rasch, X. Liu, G. Pitari, G. Di Genova, S. Y. Zhao, Y. Balkanski, S. E. Bauer, G. S. Faluvegi, H. Kokkola, R. V. Martin, J. R. Pierce, M. Schulz, D. Shindell, H. Tost, and H. Zhang
Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, https://doi.org/10.5194/acp-16-3525-2016, 2016
Short summary
Short summary
Processes affecting aerosol removal from the atmosphere are not fully understood. In this study we investigate to what extent atmospheric transport models can reproduce observed loss of aerosols. We compare measurements of radioactive isotopes, that attached to ambient sulfate aerosols during the 2011 Fukushima nuclear accident, to 19 models using identical emissions. Results indicate aerosol removal that is too fast in most models, and apply to aerosols that have undergone long-range transport.
Zak Kipling, Philip Stier, Colin E. Johnson, Graham W. Mann, Nicolas Bellouin, Susanne E. Bauer, Tommi Bergman, Mian Chin, Thomas Diehl, Steven J. Ghan, Trond Iversen, Alf Kirkevåg, Harri Kokkola, Xiaohong Liu, Gan Luo, Twan van Noije, Kirsty J. Pringle, Knut von Salzen, Michael Schulz, Øyvind Seland, Ragnhild B. Skeie, Toshihiko Takemura, Kostas Tsigaridis, and Kai Zhang
Atmos. Chem. Phys., 16, 2221–2241, https://doi.org/10.5194/acp-16-2221-2016, https://doi.org/10.5194/acp-16-2221-2016, 2016
Short summary
Short summary
The vertical distribution of atmospheric aerosol is an important factor in its effects on climate. In this study we use a sophisticated model of the many interacting processes affecting aerosol in the atmosphere to show that the vertical distribution is typically dominated by only a few of these processes. Constraining these physical processes may help to reduce the large differences between models. However, the important processes are not always the same for different types of aerosol.
A. Laakso, H. Kokkola, A.-I. Partanen, U. Niemeier, C. Timmreck, K. E. J. Lehtinen, H. Hakkarainen, and H. Korhonen
Atmos. Chem. Phys., 16, 305–323, https://doi.org/10.5194/acp-16-305-2016, https://doi.org/10.5194/acp-16-305-2016, 2016
Short summary
Short summary
We have studied the impacts of a volcanic eruption during solar radiation management (SRM) using an aerosol-climate model ECHAM5-HAM-SALSA and an Earth system model MPI-ESM. A volcanic eruption during stratospheric sulfur geoengineering would lead to larger particles and smaller amount of new particles than if an volcano erupts in normal atmospheric conditions. Thus, volcanic eruption during SRM would lead to only a small additional cooling which would last for a significantly shorter period.
A. Arola, G. L. Schuster, M. R. A. Pitkänen, O. Dubovik, H. Kokkola, A. V. Lindfors, T. Mielonen, T. Raatikainen, S. Romakkaniemi, S. N. Tripathi, and H. Lihavainen
Atmos. Chem. Phys., 15, 12731–12740, https://doi.org/10.5194/acp-15-12731-2015, https://doi.org/10.5194/acp-15-12731-2015, 2015
Short summary
Short summary
There have been relatively few measurement-based estimates for the direct radiative effect of brown carbon so far. This is first time that the direct radiative effect of brown carbon is estimated by exploiting the AERONET-retrieved imaginary indices. We estimated it for four sites in the Indo-Gangetic Plain: Karachi, Lahore,
Kanpur and Gandhi College.
H. Eskes, V. Huijnen, A. Arola, A. Benedictow, A.-M. Blechschmidt, E. Botek, O. Boucher, I. Bouarar, S. Chabrillat, E. Cuevas, R. Engelen, H. Flentje, A. Gaudel, J. Griesfeller, L. Jones, J. Kapsomenakis, E. Katragkou, S. Kinne, B. Langerock, M. Razinger, A. Richter, M. Schultz, M. Schulz, N. Sudarchikova, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Geosci. Model Dev., 8, 3523–3543, https://doi.org/10.5194/gmd-8-3523-2015, https://doi.org/10.5194/gmd-8-3523-2015, 2015
Short summary
Short summary
The MACC project is preparing the operational atmosphere service of the European Copernicus Programme, and uses data assimilation to combine atmospheric models with available observations. Our paper provides an overview of the aerosol and trace gas validation activity of MACC. Topics are the validation requirements, the measurement data, the assimilation systems, the upgrade procedure, operational aspects and the scoring methods. A summary is provided of recent results, including special events.
S. Fadnavis, K. Semeniuk, M. G. Schultz, M. Kiefer, A. Mahajan, L. Pozzoli, and S. Sonbawane
Atmos. Chem. Phys., 15, 11477–11499, https://doi.org/10.5194/acp-15-11477-2015, https://doi.org/10.5194/acp-15-11477-2015, 2015
Short summary
Short summary
The model and MIPAS satellite data show that there are three regions which contribute substantial pollution to the south Asian UTLS: the Asian summer monsoon (ASM), the North American monsoon (NAM) and the West African monsoon (WAM). However, penetration due to ASM convection reaches deeper into the UTLS compared to NAM and WAM outflow. Simulations show that westerly winds drive North American and European pollutants eastward where they can become part of the ASM and lifted to LS.
E. Katragkou, P. Zanis, A. Tsikerdekis, J. Kapsomenakis, D. Melas, H. Eskes, J. Flemming, V. Huijnen, A. Inness, M. G. Schultz, O. Stein, and C. S. Zerefos
Geosci. Model Dev., 8, 2299–2314, https://doi.org/10.5194/gmd-8-2299-2015, https://doi.org/10.5194/gmd-8-2299-2015, 2015
Short summary
Short summary
This work is an extended evaluation of near-surface ozone as part of the global reanalysis of atmospheric composition, produced within the European-funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003-2012 and provides an overall assessment of the modelling system performance with respect to near surface ozone for specific European subregions.
M. A. Thomas, M. Kahnert, C. Andersson, H. Kokkola, U. Hansson, C. Jones, J. Langner, and A. Devasthale
Geosci. Model Dev., 8, 1885–1898, https://doi.org/10.5194/gmd-8-1885-2015, https://doi.org/10.5194/gmd-8-1885-2015, 2015
Short summary
Short summary
We have showed that a coupled modelling system is beneficial in the sense that more complex processes can be included to better represent the aerosol processes starting from their formation, their interactions with clouds and provide better estimate of radiative forcing. Using this model set up, we estimated an annual mean 'indirect' radiative forcing of -0.64W/m2. This means that aerosols, solely by their capability of altering the microphysical properties of clouds can cool the Earth system.
H. Fischer, A. Pozzer, T. Schmitt, P. Jöckel, T. Klippel, D. Taraborrelli, and J. Lelieveld
Atmos. Chem. Phys., 15, 6971–6980, https://doi.org/10.5194/acp-15-6971-2015, https://doi.org/10.5194/acp-15-6971-2015, 2015
A. Inness, A.-M. Blechschmidt, I. Bouarar, S. Chabrillat, M. Crepulja, R. J. Engelen, H. Eskes, J. Flemming, A. Gaudel, F. Hendrick, V. Huijnen, L. Jones, J. Kapsomenakis, E. Katragkou, A. Keppens, B. Langerock, M. de Mazière, D. Melas, M. Parrington, V. H. Peuch, M. Razinger, A. Richter, M. G. Schultz, M. Suttie, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Atmos. Chem. Phys., 15, 5275–5303, https://doi.org/10.5194/acp-15-5275-2015, https://doi.org/10.5194/acp-15-5275-2015, 2015
Short summary
Short summary
The paper presents results from data assimilation studies with the new Composition-IFS model developed in the MACC project. This system was used in MACC to produce daily analyses and 5-day forecasts of atmospheric composition and is now run daily in the EU’s Copernicus Atmosphere Monitoring Service. The paper looks at the quality of the CO, O3 and NO2 analysis fields obtained with this system, comparing them against observations, a control run and an older version of the model.
J. Flemming, V. Huijnen, J. Arteta, P. Bechtold, A. Beljaars, A.-M. Blechschmidt, M. Diamantakis, R. J. Engelen, A. Gaudel, A. Inness, L. Jones, B. Josse, E. Katragkou, V. Marecal, V.-H. Peuch, A. Richter, M. G. Schultz, O. Stein, and A. Tsikerdekis
Geosci. Model Dev., 8, 975–1003, https://doi.org/10.5194/gmd-8-975-2015, https://doi.org/10.5194/gmd-8-975-2015, 2015
Short summary
Short summary
We describe modules for atmospheric chemistry, wet and dry deposition and lightning NO production, which have been newly introduced in ECMWF's weather forecasting model. With that model, we want to forecast global air pollution as part of the European Copernicus Atmosphere Monitoring Service. We show that the new model results compare as well or better with in situ and satellite observations of ozone, CO, NO2, SO2 and formaldehyde as the previous model.
R. Paugam, M. Wooster, J. Atherton, S. R. Freitas, M. G. Schultz, and J. W. Kaiser
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-9815-2015, https://doi.org/10.5194/acpd-15-9815-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
The transport of Biomass Burning emissions in Chemical Transport Model rely on parametrization of plumes injection height. Using fire observation selected to ensure match-up of fire-atmosphere-plume dynamics; a popular plume rise model was improved and optimized. The resulting model shows response to the effect of atmospheric stability consistent with previous findings and is able to predict higher injection height than any other tested parametrizations, giving a closer match with observation.
K. Lefever, R. van der A, F. Baier, Y. Christophe, Q. Errera, H. Eskes, J. Flemming, A. Inness, L. Jones, J.-C. Lambert, B. Langerock, M. G. Schultz, O. Stein, A. Wagner, and S. Chabrillat
Atmos. Chem. Phys., 15, 2269–2293, https://doi.org/10.5194/acp-15-2269-2015, https://doi.org/10.5194/acp-15-2269-2015, 2015
Short summary
Short summary
We validate and discuss the analyses of stratospheric ozone delivered in near-real time between 2009 and 2012 by four different data assimilation systems: IFS-MOZART, BASCOE, SACADA and TM3DAM. It is shown that the characteristics of the assimilation systems are much less important than those of the assimilated data sets. A correct representation of the vertical distribution of ozone requires satellite observations which are well resolved vertically and extend into the lowermost stratosphere.
C. Andersson, R. Bergström, C. Bennet, L. Robertson, M. Thomas, H. Korhonen, K. E. J. Lehtinen, and H. Kokkola
Geosci. Model Dev., 8, 171–189, https://doi.org/10.5194/gmd-8-171-2015, https://doi.org/10.5194/gmd-8-171-2015, 2015
Short summary
Short summary
We have integrated the sectional aerosol dynamics model SALSA into the European scale chemistry-transport model MATCH. The combined model reproduces observed higher particle number concentration (PNCs) in central Europe and lower concentrations in remote regions; however, the total PNC is underestimated. The low nucleation rate coefficient used in this study is an important reason for the underestimation.
E. M. Dunne, S. Mikkonen, H. Kokkola, and H. Korhonen
Atmos. Chem. Phys., 14, 13631–13642, https://doi.org/10.5194/acp-14-13631-2014, https://doi.org/10.5194/acp-14-13631-2014, 2014
Short summary
Short summary
Marine clouds have a strong effect on the Earth's radiative balance. One proposed climate feedback is that, in a warming climate, marine aerosol emissions will change due to changing wind speeds. We have examined the processes that affect aerosol emissions and removal over 15 years, and high-temporal-resolution output over 2 months. We conclude that wind trends are unlikely to cause a strong feedback in marine regions, but changes in removal processes or transport from continental regions may.
S. Fadnavis, M. G. Schultz, K. Semeniuk, A. S. Mahajan, L. Pozzoli, S. Sonbawne, S. D. Ghude, M. Kiefer, and E. Eckert
Atmos. Chem. Phys., 14, 12725–12743, https://doi.org/10.5194/acp-14-12725-2014, https://doi.org/10.5194/acp-14-12725-2014, 2014
Short summary
Short summary
The Asian summer monsoon transports pollutants from local emission sources to the upper troposphere and lower stratosphere (UTLS). The increasing trend of these pollutants may have climatic impact. This study addresses the impact of convectively lifted Indian and Chinese emissions on the ULTS. Sensitivity experiments with emission changes in particular regions show that Chinese emissions have a greater impact on the concentrations of NOY species than Indian emissions.
A.-I. Partanen, E. M. Dunne, T. Bergman, A. Laakso, H. Kokkola, J. Ovadnevaite, L. Sogacheva, D. Baisnée, J. Sciare, A. Manders, C. O'Dowd, G. de Leeuw, and H. Korhonen
Atmos. Chem. Phys., 14, 11731–11752, https://doi.org/10.5194/acp-14-11731-2014, https://doi.org/10.5194/acp-14-11731-2014, 2014
Short summary
Short summary
New parameterizations for the sea spray aerosol source flux and its organic fraction were incorporated into a global aerosol-climate model. The emissions of sea salt were considerably less than previous estimates. This study demonstrates that sea spray aerosol may actually decrease the number of cloud droplets, which has a warming effect on climate. Overall, sea spray aerosol was predicted to have a global cooling effect due to the scattering of solar radiation from sea spray aerosol particles.
K. Tsigaridis, N. Daskalakis, M. Kanakidou, P. J. Adams, P. Artaxo, R. Bahadur, Y. Balkanski, S. E. Bauer, N. Bellouin, A. Benedetti, T. Bergman, T. K. Berntsen, J. P. Beukes, H. Bian, K. S. Carslaw, M. Chin, G. Curci, T. Diehl, R. C. Easter, S. J. Ghan, S. L. Gong, A. Hodzic, C. R. Hoyle, T. Iversen, S. Jathar, J. L. Jimenez, J. W. Kaiser, A. Kirkevåg, D. Koch, H. Kokkola, Y. H Lee, G. Lin, X. Liu, G. Luo, X. Ma, G. W. Mann, N. Mihalopoulos, J.-J. Morcrette, J.-F. Müller, G. Myhre, S. Myriokefalitakis, N. L. Ng, D. O'Donnell, J. E. Penner, L. Pozzoli, K. J. Pringle, L. M. Russell, M. Schulz, J. Sciare, Ø. Seland, D. T. Shindell, S. Sillman, R. B. Skeie, D. Spracklen, T. Stavrakou, S. D. Steenrod, T. Takemura, P. Tiitta, S. Tilmes, H. Tost, T. van Noije, P. G. van Zyl, K. von Salzen, F. Yu, Z. Wang, Z. Wang, R. A. Zaveri, H. Zhang, K. Zhang, Q. Zhang, and X. Zhang
Atmos. Chem. Phys., 14, 10845–10895, https://doi.org/10.5194/acp-14-10845-2014, https://doi.org/10.5194/acp-14-10845-2014, 2014
O. Stein, M. G. Schultz, I. Bouarar, H. Clark, V. Huijnen, A. Gaudel, M. George, and C. Clerbaux
Atmos. Chem. Phys., 14, 9295–9316, https://doi.org/10.5194/acp-14-9295-2014, https://doi.org/10.5194/acp-14-9295-2014, 2014
K. Hens, A. Novelli, M. Martinez, J. Auld, R. Axinte, B. Bohn, H. Fischer, P. Keronen, D. Kubistin, A. C. Nölscher, R. Oswald, P. Paasonen, T. Petäjä, E. Regelin, R. Sander, V. Sinha, M. Sipilä, D. Taraborrelli, C. Tatum Ernest, J. Williams, J. Lelieveld, and H. Harder
Atmos. Chem. Phys., 14, 8723–8747, https://doi.org/10.5194/acp-14-8723-2014, https://doi.org/10.5194/acp-14-8723-2014, 2014
S. Fadnavis, K. Semeniuk, M. G. Schultz, A. Mahajan, L. Pozzoli, S. Sonbawane, and M. Kiefer
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-20159-2014, https://doi.org/10.5194/acpd-14-20159-2014, 2014
Revised manuscript not accepted
G. W. Mann, K. S. Carslaw, C. L. Reddington, K. J. Pringle, M. Schulz, A. Asmi, D. V. Spracklen, D. A. Ridley, M. T. Woodhouse, L. A. Lee, K. Zhang, S. J. Ghan, R. C. Easter, X. Liu, P. Stier, Y. H. Lee, P. J. Adams, H. Tost, J. Lelieveld, S. E. Bauer, K. Tsigaridis, T. P. C. van Noije, A. Strunk, E. Vignati, N. Bellouin, M. Dalvi, C. E. Johnson, T. Bergman, H. Kokkola, K. von Salzen, F. Yu, G. Luo, A. Petzold, J. Heintzenberg, A. Clarke, J. A. Ogren, J. Gras, U. Baltensperger, U. Kaminski, S. G. Jennings, C. D. O'Dowd, R. M. Harrison, D. C. S. Beddows, M. Kulmala, Y. Viisanen, V. Ulevicius, N. Mihalopoulos, V. Zdimal, M. Fiebig, H.-C. Hansson, E. Swietlicki, and J. S. Henzing
Atmos. Chem. Phys., 14, 4679–4713, https://doi.org/10.5194/acp-14-4679-2014, https://doi.org/10.5194/acp-14-4679-2014, 2014
H. Kokkola, P. Yli-Pirilä, M. Vesterinen, H. Korhonen, H. Keskinen, S. Romakkaniemi, L. Hao, A. Kortelainen, J. Joutsensaari, D. R. Worsnop, A. Virtanen, and K. E. J. Lehtinen
Atmos. Chem. Phys., 14, 1689–1700, https://doi.org/10.5194/acp-14-1689-2014, https://doi.org/10.5194/acp-14-1689-2014, 2014
A. Basu, M. G. Schultz, S. Schröder, L. Francois, X. Zhang, G. Lohmann, and T. Laepple
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-3193-2014, https://doi.org/10.5194/acpd-14-3193-2014, 2014
Revised manuscript not accepted
T. Korhola, H. Kokkola, H. Korhonen, A.-I. Partanen, A. Laaksonen, K. E. J. Lehtinen, and S. Romakkaniemi
Geosci. Model Dev., 7, 161–174, https://doi.org/10.5194/gmd-7-161-2014, https://doi.org/10.5194/gmd-7-161-2014, 2014
A. Lipponen, V. Kolehmainen, S. Romakkaniemi, and H. Kokkola
Geosci. Model Dev., 6, 2087–2098, https://doi.org/10.5194/gmd-6-2087-2013, https://doi.org/10.5194/gmd-6-2087-2013, 2013
A. I. Partanen, A. Laakso, A. Schmidt, H. Kokkola, T. Kuokkanen, J.-P. Pietikäinen, V.-M. Kerminen, K. E. J. Lehtinen, L. Laakso, and H. Korhonen
Atmos. Chem. Phys., 13, 12059–12071, https://doi.org/10.5194/acp-13-12059-2013, https://doi.org/10.5194/acp-13-12059-2013, 2013
S. Fadnavis, K. Semeniuk, L. Pozzoli, M. G. Schultz, S. D. Ghude, S. Das, and R. Kakatkar
Atmos. Chem. Phys., 13, 8771–8786, https://doi.org/10.5194/acp-13-8771-2013, https://doi.org/10.5194/acp-13-8771-2013, 2013
A. Inness, F. Baier, A. Benedetti, I. Bouarar, S. Chabrillat, H. Clark, C. Clerbaux, P. Coheur, R. J. Engelen, Q. Errera, J. Flemming, M. George, C. Granier, J. Hadji-Lazaro, V. Huijnen, D. Hurtmans, L. Jones, J. W. Kaiser, J. Kapsomenakis, K. Lefever, J. Leitão, M. Razinger, A. Richter, M. G. Schultz, A. J. Simmons, M. Suttie, O. Stein, J.-N. Thépaut, V. Thouret, M. Vrekoussis, C. Zerefos, and the MACC team
Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, https://doi.org/10.5194/acp-13-4073-2013, 2013
Related subject area
Climate and Earth system modeling
An emulation-based approach for interrogating reactive transport models
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Truly Conserving with Conservative Remapping Methods
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
WRF (v4.0)-SUEWS (v2018c) Coupled System: Development, Evaluation and Application
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Resolving the mesoscale at reduced computational cost with FESOM 2.5: efficient modeling approaches applied to the Southern Ocean
Modeling and evaluating the effects of irrigation on land-atmosphere interaction in South-West Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Deep Learning Model based on Multi-scale Feature Fusion for Precipitation Nowcasting
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Karl E. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-177, https://doi.org/10.5194/gmd-2023-177, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved primarily due to the re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-117, https://doi.org/10.5194/gmd-2023-117, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1496, https://doi.org/10.5194/egusphere-2023-1496, 2023
Short summary
Short summary
Ocean models struggle to simulate small-scale ocean flows due to the computational cost of high-resolution simulations. Several cost-reducing strategies are applied to simulations of the Southern Ocean and evaluated with respect to observations and traditional, lower-resolution modelling methods. The high-resolution simulations effectively reproduce small-scale flows seen in satellite data and are largely consistent with traditional model simulations regarding their response to climate change.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
EGUsphere, https://doi.org/10.5194/egusphere-2023-890, https://doi.org/10.5194/egusphere-2023-890, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The caused effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which is simulating the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-109, https://doi.org/10.5194/gmd-2023-109, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. This study present a deep learning architecture MFF to improve the forecast skills of precipitations especially for heavy precipitations. 2. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. 3. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors, so that heavy precipitations are produced.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio Bento, and Angelina Bushenkova
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-136, https://doi.org/10.5194/gmd-2023-136, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data, and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Cited articles
Aiken, A. C., Salcedo, D., Cubison, M. J., Huffman, J. A., DeCarlo, P. F.,
Ulbrich, I. M., Docherty, K. S., Sueper, D., Kimmel, J. R., Worsnop, D. R.,
Trimborn, A., Northway, M., Stone, E. A., Schauer, J. J., Volkamer, R. M.,
Fortner, E., de Foy, B., Wang, J., Laskin, A., Shutthanandan, V., Zheng, J.,
Zhang, R., Gaffney, J., Marley, N. A., Paredes-Miranda, G., Arnott, W. P.,
Molina, L. T., Sosa, G., and Jimenez, J. L.: Mexico City aerosol analysis
during MILAGRO using high resolution aerosol mass spectrometry at the urban
supersite (T0) – Part 1: Fine particle composition and organic source
apportionment, Atmos. Chem. Phys., 9, 6633–6653,
https://doi.org/10.5194/acp-9-6633-2009, 2009. a, b
Aiken, A. C., de Foy, B., Wiedinmyer, C., DeCarlo, P. F., Ulbrich, I. M.,
Wehrli, M. N., Szidat, S., Prevot, A. S. H., Noda, J., Wacker, L., Volkamer,
R., Fortner, E., Wang, J., Laskin, A., Shutthanandan, V., Zheng, J., Zhang,
R., Paredes-Miranda, G., Arnott, W. P., Molina, L. T., Sosa, G., Querol, X.,
and Jimenez, J. L.: Mexico city aerosol analysis during MILAGRO using high
resolution aerosol mass spectrometry at the urban supersite (T0) – Part 2:
Analysis of the biomass burning contribution and the non-fossil carbon
fraction, Atmos. Chem. Phys., 10, 5315–5341,
https://doi.org/10.5194/acp-10-5315-2010, 2010. a
Athanasopoulou, E., Vogel, H., Vogel, B., Tsimpidi, A. P., Pandis, S. N.,
Knote, C., and Fountoukis, C.: Modeling the meteorological and chemical
effects of secondary organic aerosols during an EUCAARI campaign, Atmos.
Chem. Phys., 13, 625–645, https://doi.org/10.5194/acp-13-625-2013, 2013. a
Barley, M. H. and McFiggans, G.: The critical assessment of vapour pressure
estimation methods for use in modelling the formation of atmospheric organic
aerosol, Atmos. Chem. Phys., 10, 749–767,
https://doi.org/10.5194/acp-10-749-2010, 2010. a, b, c
Bergman, T., Kerminen, V.-M., Korhonen, H., Lehtinen, K. J., Makkonen, R.,
Arola, A., Mielonen, T., Romakkaniemi, S., Kulmala, M., and Kokkola, H.:
Evaluation of the sectional aerosol microphysics module SALSA implementation
in ECHAM5-HAM aerosol-climate model, Geosci. Model Dev., 5, 845–868,
https://doi.org/10.5194/gmd-5-845-2012, 2012. a, b
Canagaratna, M. R., Jimenez, J. L., Kroll, J. H., Chen, Q., Kessler, S. H.,
Massoli, P., Hildebrandt Ruiz, L., Fortner, E., Williams, L. R., Wilson, K.
R., Surratt, J. D., Donahue, N. M., Jayne, J. T., and Worsnop, D. R.:
Elemental ratio measurements of organic compounds using aerosol mass
spectrometry: characterization, improved calibration, and implications,
Atmos. Chem. Phys., 15, 253–272, https://doi.org/10.5194/acp-15-253-2015,
2015. a
Chen, Q., Farmer, D., Schneider, J., Zorn, S., Heald, C., Karl, T., Guenther,
A., Allan, J., Robinson, N., Coe, H., Kimmel, J. R., Pauliquevis, T.,
Borrmann, S., Pöschl, U., Andreae, M. O., Artaxo, P., Jimenez, J. L., and
Martin, S. T.: Mass spectral characterization of submicron biogenic organic
particles in the Amazon Basin, Geophys. Res. Lett., 36, L20806,
https://doi.org/10.1029/2009GL039880, 2009. a
Clegg, S. L., Brimblecombe, P., and Wexler, A. S.: Thermodynamic Model of the
System H+ – – Na+ – –
NO3 – Cl– H2O at 298.15 K, J. Phys. Chem. A, 102,
2155–2171, 1998. a
Clements, A. L. and Seinfeld, J. H.: Detection and quantification of
2-methyltetrols in ambient aerosol in the southeastern United States, Atmos.
Environ., 41, 1825–1830, 2007. a
Cole-Filipiak, N. C., OConnor, A. E., and Elrod, M. J.: Kinetics of the
hydrolysis of atmospherically relevant isoprene-derived hydroxy epoxides,
Environ. Sci. Technol., 44, 6718–6723, 2010. a
Crounse, J. D., Paulot, F., Kjaergaard, H. G., and Wennberg, P. O.: Peroxy
radical isomerization in the oxidation of isoprene, Phys. Chem. Chem. Phys.,
13, 13607–13613, 2011. a
Dall'Osto, M., Ceburnis, D., Martucci, G., Bialek, J., Dupuy, R., Jennings,
S. G., Berresheim, H., Wenger, J., Healy, R., Facchini, M. C., Rinaldi, M.,
Giulianelli, L., Finessi, E., Worsnop, D., Ehn, M., Mikkilä, J., Kulmala,
M., and O'Dowd, C. D.: Aerosol properties associated with air masses arriving
into the North East Atlantic during the 2008 Mace Head EUCAARI intensive
observing period: an overview, Atmos. Chem. Phys., 10, 8413–8435,
https://doi.org/10.5194/acp-10-8413-2010, 2010. a, b
D'Ambro, E. L., Lee, B. H., Liu, J., Shilling, J. E., Gaston, C. J.,
Lopez-Hilfiker, F. D., Schobesberger, S., Zaveri, R. A., Mohr, C., Lutz, A.,
Zhang, Z., Gold, A., Surratt, J. D., Rivera-Rios, J. C., Keutsch, F. N., and
Thornton, J. A.: Molecular composition and volatility of isoprene
photochemical oxidation secondary organic aerosol under low- and high-NOx
conditions, Atmos. Chem. Phys., 17, 159–174,
https://doi.org/10.5194/acp-17-159-2017, 2017a. a, b, c, d, e
D'Ambro, E. L., Møller, K. H., Lopez-Hilfiker, F. D., Schobesberger, S.,
Liu, J., Shilling, J. E., Lee, B. H., Kjaergaard, H. G., and Thornton, J. A.:
Isomerization of Second-Generation Isoprene Peroxy Radicals: Epoxide
Formation and Implications for Secondary Organic Aerosol Yields, Environ.
Sci. Technol., 51, 4978–4987, 2017b. a, b, c, d, e, f
De Gouw, J. and Jimenez, J. L.: Organic aerosols in the Earths atmosphere,
Environ. Sci. Technol., 43, 7614–7618, 2009. a
Dentener, F., Kinne, S., Bond, T., Boucher, O., Cofala, J., Generoso, S.,
Ginoux, P., Gong, S., Hoelzemann, J. J., Ito, A., Marelli, L., Penner, J. E.,
Putaud, J.-P., Textor, C., Schulz, M., van der Werf, G. R., and Wilson, J.:
Emissions of primary aerosol and precursor gases in the years 2000 and 1750
prescribed data-sets for AeroCom, Atmos. Chem. Phys., 6, 4321–4344,
https://doi.org/10.5194/acp-6-4321-2006, 2006. a
Donahue, N., Robinson, A., Stanier, C., and Pandis, S.: Coupled partitioning,
dilution, and chemical aging of semivolatile organics, Environ. Sci.
Technol., 40, 2635–2643, 2006. a
Donahue, N. M., Kroll, J. H., Pandis, S. N., and Robinson, A. L.: A
two-dimensional volatility basis set – Part 2: Diagnostics of
organic-aerosol evolution, Atmos. Chem. Phys., 12, 615–634,
https://doi.org/10.5194/acp-12-615-2012, 2012. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G.,
Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando,
J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.:
Description and evaluation of the Model for Ozone and Related chemical
Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67,
https://doi.org/10.5194/gmd-3-43-2010, 2010. a
Epstein, S. A., Riipinen, I., and Donahue, N. M.: A semiempirical correlation
between enthalpy of vaporization and saturation concentration for organic
aerosol, Environ. Sci. Technol., 44, 743–748, 2009. a
Ervens, B. and Volkamer, R.: Glyoxal processing by aerosol multiphase
chemistry: towards a kinetic modeling framework of secondary organic aerosol
formation in aqueous particles, Atmos. Chem. Phys., 10, 8219–8244,
https://doi.org/10.5194/acp-10-8219-2010, 2010. a, b
Farina, S. C., Adams, P. J., and Pandis, S. N.: Modeling global secondary
organic aerosol formation and processing with the volatility basis set:
Implications for anthropogenic secondary organic aerosol, J. Geophys.
Res.-Atmos., 115, D09202, https://doi.org/10.1029/2009JD013046, 2010. a, b, c, d, e, f, g, h
Fröhlich-Nowoisky, J., Kampf, C. J., Weber, B., Huffman, J. A.,
Pöhlker, C., Andreae, M. O., Lang-Yona, N., Burrows, S. M., Gunthe,
S. S., Elbert, W., et al.: Bioaerosols in the Earth system: Climate, health,
and ecosystem interactions, Atmos. Res., 182, 346–376, 2016. a
Fu, T.-M., Jacob, D. J., Wittrock, F., Burrows, J. P., Vrekoussis, M., and
Henze, D. K.: Global budgets of atmospheric glyoxal and methylglyoxal, and
implications for formation of secondary organic aerosols, J. Geophys.
Res.-Atmos., 113, F15303, https://doi.org/10.1029/2007JD009505, 2008. a, b
Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon,
H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U.,
Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G.,
Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.:
Particulate matter, air quality and climate: lessons learned and future
needs, Atmos. Chem. Phys., 15, 8217–8299,
https://doi.org/10.5194/acp-15-8217-2015, 2015. a, b
Ghan, S. J. and Schwartz, S. E.: Aerosol properties and processes: A path
from field and laboratory measurements to global climate models, B. Am.
Meteorol. Soc., 88, 1059–1083, 2007. a
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron,
C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of
Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6,
3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006. a, b, c
Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D.,
Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H.,
Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M.
E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel,
Th. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D.,
Szmigielski, R., and Wildt, J.: The formation, properties and impact of
secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys.,
9, 5155–5236, https://doi.org/10.5194/acp-9-5155-2009, 2009. a
Heald, C. L., Jacob, D. J., Park, R. J., Russell, L. M., Huebert, B. J.,
Seinfeld, J. H., Liao, H., and Weber, R. J.: A large organic aerosol source
in the free troposphere missing from current models, Geophys. Res. Lett., 32,
L18809, https://doi.org/10.1029/2005GL023831, 2005. a
Henrot, A.-J., Stanelle, T., Schröder, S., Siegenthaler, C.,
Taraborrelli, D., and Schultz, M. G.: Implementation of the MEGAN (v2.1)
biogenic emission model in the ECHAM6-HAMMOZ chemistry climate model, Geosci.
Model Dev., 10, 903–926, https://doi.org/10.5194/gmd-10-903-2017, 2017. a, b
Hodzic, A., Madronich, S., Kasibhatla, P. S., Tyndall, G., Aumont, B.,
Jimenez, J. L., Lee-Taylor, J., and Orlando, J.: Organic photolysis reactions
in tropospheric aerosols: effect on secondary organic aerosol formation and
lifetime, Atmos. Chem. Phys., 15, 9253–9269,
https://doi.org/10.5194/acp-15-9253-2015, 2015. a, b, c, d
Hodzic, A., Kasibhatla, P. S., Jo, D. S., Cappa, C. D., Jimenez, J. L.,
Madronich, S., and Park, R. J.: Rethinking the global secondary organic
aerosol (SOA) budget: stronger production, faster removal, shorter lifetime,
Atmos. Chem. Phys., 16, 7917–7941, https://doi.org/10.5194/acp-16-7917-2016,
2016. a, b, c, d, e, f, g
IPCC: Annex I: Atlas of Global and Regional Climate Projections, Cambridge
University Press, Cambridge, UK, New York, NY, USA, book section AI,
1311–1394, https://doi.org/10.1017/CBO9781107415324.029, 2013. a
Jimenez, J., Canagaratna, M., Donahue, N., et al.: Evolution of organic
aerosols in the atmosphere, Science, 326, 1525–1529, 2009. a
Jülich Supercomputing Centre: JURECA: General-purpose supercomputer at
Jülich Supercomputing Centre, J. Large-scale Res. Fac., 2, A62,
https://doi.org/10.17815/jlsrf-2-121, 2016. a
Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J.,
Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J.,
Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat,
G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati, E.,
Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate
modelling: a review, Atmos. Chem. Phys., 5, 1053–1123,
https://doi.org/10.5194/acp-5-1053-2005, 2005. a, b
Kavouras, I. G., Mihalopoulos, N., and Stephanou, E. G.: Secondary organic
aerosol formation vs primary organic aerosol emission: In situ evidence for
the chemical coupling between monoterpene acidic photooxidation products and
new particle formation over forests, Environ. Sci. Technol., 33, 1028–1037,
1999. a
Kinnison, D., Brasseur, G., Walters, S., Garcia, R., Marsh, D., Sassi, F.,
Harvey, V., Randall, C., Emmons, L., Lamarque, J., Hess, P., Orlando, J. J.,
Tie, X. X., Randel, W., Pan, L. L., Gettelman, A., Granier, C., Diehl, T.,
Niemeier, U., and Simmons, A. J.: Sensitivity of chemical tracers to
meteorological parameters in the MOZART-3 chemical transport model, J.
Geophys. Res.-Atmos., 112, D20302, https://doi.org/10.1029/2006JD007879, 2007. a
Kokkola, H., Korhonen, H., Lehtinen, K. E. J., Makkonen, R., Asmi, A.,
Järvenoja, S., Anttila, T., Partanen, A.-I., Kulmala, M., Järvinen,
H., Laaksonen, A., and Kerminen, V.-M.: SALSA – a Sectional Aerosol module
for Large Scale Applications, Atmos. Chem. Phys., 8, 2469–2483,
https://doi.org/10.5194/acp-8-2469-2008, 2008. a, b
Kokkola, H., Kühn, T., Laakso, A., Bergman, T., Lehtinen, K. E. J.,
Mielonen, T., Arola, A., Stadtler, S., Korhonen, H., Ferrachat, S., Lohmann,
U., Neubauer, D., Tegen, I., Siegenthaler-Le Drian, C., Schultz, M. G., Bey,
I., Stier, P., Daskalakis, N., Heald, C. L., and Romakkaniemi, S.: SALSA2.0:
The sectional aerosol module of the aerosol-chemistry-climate model
ECHAM6.3.0-HAM2.3-MOZ1.0, Geosci. Model Dev. Discuss.,
https://doi.org/10.5194/gmd-2018-47, in review, 2018. a, b
Kourtchev, I., Ruuskanen, T., Maenhaut, W., Kulmala, M., and Claeys, M.:
Observation of 2-methyltetrols and related photo-oxidation products of
isoprene in boreal forest aerosols from Hyytiälä, Finland, Atmos.
Chem. Phys., 5, 2761–2770, https://doi.org/10.5194/acp-5-2761-2005, 2005. a
Kühn, T., Merikanto, J., Mielonen, T., Stadtler, S., Hienola, A.,
Korhonen, H., Ferrachat, S., Lohmann, U., Neubauer, D., Tegen, I.,
Siegenthaler-Le Drian, C., Wahl, S., Schultz, M. G., Rast, S., Schmidt, H.,
Stier, P., Lehtinen, K., and Kokkola, H.: SALSA2.0 part2: Implementation of a
volatility basis set to model formation of secondary organic aerosol, Geosci.
Model Dev., in preparation, 2018. a
Kurten, T., Tiusanen, K., Roldin, P., Rissanen, M., Luy, J.-N., Boy, M., Ehn,
M., and Donahue, N.: α-Pinene autoxidation products may not have
extremely low saturation vapor pressures despite high O : C ratios, J.
Phys. Chem. A, 120, 2569–2582, 2016. a
Lakey, P. S., Berkemeier, T., Tong, H., Arangio, A. M., Lucas, K.,
Pöschl, U., and Shiraiwa, M.: Chemical exposure-response relationship
between air pollutants and reactive oxygen species in the human respiratory
tract, Sci. Rep.-UK, 6, 32916, https://doi.org/10.1038/srep32916, 2016. a
Lal, V., Khalizov, A. F., Lin, Y., Galvan, M. D., Connell, B. T., and Zhang,
R.: Heterogeneous reactions of epoxides in acidic media, J. Phys. Chem. A,
116, 6078–6090, 2012. a
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z.,
Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D.,
Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M.,
Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.:
Historical (1850–2000) gridded anthropogenic and biomass burning emissions
of reactive gases and aerosols: methodology and application, Atmos. Chem.
Phys., 10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010, 2010. a, b
Lelieveld, J., Gromov, S., Pozzer, A., and Taraborrelli, D.: Global
tropospheric hydroxyl distribution, budget and reactivity, Atmos. Chem.
Phys., 16, 12477–12493, https://doi.org/10.5194/acp-16-12477-2016, 2016. a, b
Liggio, J., Li, S.-M., and McLaren, R.: Reactive uptake of glyoxal by
particulate matter, J. Geophys. Res.-Atmos., 110, D10304,
https://doi.org/10.1029/2004JD005113, 2005b. a, b, c
Lin, G., Penner, J. E., Sillman, S., Taraborrelli, D., and Lelieveld, J.:
Global modeling of SOA formation from dicarbonyls, epoxides, organic nitrates
and peroxides, Atmos. Chem. Phys., 12, 4743–4774,
https://doi.org/10.5194/acp-12-4743-2012, 2012. a, b, c
Lin, S.-J. and Rood, R. B.: Multidimensional flux-form semi-Lagrangian
transport schemes, Mon. Weather Rev., 124, 2046–2070, 1996. a
Lin, Y.-H., Knipping, E. M., Edgerton, E. S., Shaw, S. L., and Surratt, J.
D.: Investigating the influences of SO2 and NH3 levels on
isoprene-derived secondary organic aerosol formation using conditional
sampling approaches, Atmos. Chem. Phys., 13, 8457–8470,
https://doi.org/10.5194/acp-13-8457-2013, 2013a. a, b
Lin, Y.-H., Zhang, H., Pye, H. O., Zhang, Z., Marth, W. J., Park, S.,
Arashiro, M., Cui, T., Budisulistiorini, S. H., Sexton, K. G., Vizuete, W.,
Xie, Y., Luecken, D. J., Piletic, I. R., Edney, E. O., Bartolotti, L. J.,
Gold, A., and Surratt, J. D.: Epoxide as a precursor to secondary organic
aerosol formation from isoprene photooxidation in the presence of nitrogen
oxides, P. Natl. Acad. Sci. USA, 110, 6718–6723, 2013b. a
Liu, J., DAmbro, E. L., Lee, B. H., Lopez-Hilfiker, F. D., Zaveri, R. A.,
Rivera-Rios, J. C., Keutsch, F. N., Iyer, S., Kurten, T., Zhang, Z., Gold,
A., Surratt, J. D., Shilling, J. E., and Thornton, J. A.: Efficient isoprene
secondary organic aerosol formation from a non-IEPOX pathway, Environ. Sci.
Technol., 50, 9872–9880, 2016. a, b
Lopez-Hilfiker, F., Mohr, C., DAmbro, E. L., Lutz, A., Riedel, T. P., Gaston,
C. J., Iyer, S., Zhang, Z., Gold, A., Surratt, J. D., Lee, B. H., Kurten, T.,
Hu, W. W., Jimenez, J., Hallquist, M., and Thornton, J. A.: Molecular
composition and volatility of organic aerosol in the Southeastern US:
implications for IEPOX derived SOA, Environ. Sci. Technol., 50, 2200–2209,
2016. a
Marais, E. A., Jacob, D. J., Jimenez, J. L., Campuzano-Jost, P., Day, D. A.,
Hu, W., Krechmer, J., Zhu, L., Kim, P. S., Miller, C. C., Fisher, J. A.,
Travis, K., Yu, K., Hanisco, T. F., Wolfe, G. M., Arkinson, H. L., Pye, H. O.
T., Froyd, K. D., Liao, J., and McNeill, V. F.: Aqueous-phase mechanism for
secondary organic aerosol formation from isoprene: application to the
southeast United States and co-benefit of SO2 emission controls, Atmos.
Chem. Phys., 16, 1603–1618, https://doi.org/10.5194/acp-16-1603-2016, 2016. a
Martin, S. T., Andreae, M. O., Althausen, D., Artaxo, P., Baars, H.,
Borrmann, S., Chen, Q., Farmer, D. K., Guenther, A., Gunthe, S. S., Jimenez,
J. L., Karl, T., Longo, K., Manzi, A., Müller, T., Pauliquevis, T.,
Petters, M. D., Prenni, A. J., Pöschl, U., Rizzo, L. V., Schneider, J.,
Smith, J. N., Swietlicki, E., Tota, J., Wang, J., Wiedensohler, A., and Zorn,
S. R.: An overview of the Amazonian Aerosol Characterization Experiment 2008
(AMAZE-08), Atmos. Chem. Phys., 10, 11415–11438,
https://doi.org/10.5194/acp-10-11415-2010, 2010. a, b, c
McFiggans, G., Topping, D. O., and Barley, M. H.: The sensitivity of
secondary organic aerosol component partitioning to the predictions of
component properties – Part 1: A systematic evaluation of some available
estimation techniques, Atmos. Chem. Phys., 10, 10255–10272,
https://doi.org/10.5194/acp-10-10255-2010, 2010. a
McNeill, V. F., Woo, J. L., Kim, D. D., Schwier, A. N., Wannell, N. J.,
Sumner, A. J., and Barakat, J. M.: Aqueous-phase secondary organic aerosol
and organosulfate formation in atmospheric aerosols: a modeling study,
Environ. Sci. Technol., 46, 8075–8081, 2012. a
Nannoolal, Y., Rarey, J., and Ramjugernath, D.: Estimation of pure component
properties: Part 3. Estimation of the vapor pressure of non-electrolyte
organic compounds via group contributions and group interactions, Fluid Phase
Equilibr., 269, 117–133, 2008. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
Nozière, B., González, N. J., Borg-Karlson, A.-K., Pei, Y., Redeby,
J. P., Krejci, R., Dommen, J., Prévôt, A. S., and Anthonsen, T.:
Atmospheric chemistry in stereo: A new look at secondary organic aerosols
from isoprene, Geophys. Res. Lett., 38, L11807, https://doi.org/10.1029/2011GL047323,
2011. a
O'Donnell, D., Tsigaridis, K., and Feichter, J.: Estimating the direct and
indirect effects of secondary organic aerosols using ECHAM5-HAM, Atmos. Chem.
Phys., 11, 8635–8659, https://doi.org/10.5194/acp-11-8635-2011, 2011. a
Odum, J. R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R. C., and
Seinfeld, J. H.: Gas/particle partitioning and secondary organic aerosol
yields, Environ. Sci. Technol., 30, 2580–2585, 1996. a
Pandis, S. N., Harley, R. A., Cass, G. R., and Seinfeld, J. H.: Secondary
organic aerosol formation and transport, Atmos. Environ. A-Gen., 26,
2269–2282, 1992. a
Pankow, J. F.: An absorption model of gas/particle partitioning of organic
compounds in the atmosphere, Atmos. Environ., 28, 185–188, 1994. a
Pöschl, U., Martin, S., Sinha, B., Chen, Q., Gunthe, S., Huffman, J.,
Borrmann, S., Farmer, D., Garland, R., Helas, G., Jimenez, J. L., King, S.
M., Manzi, A., Mikhailov, E., Pauliquevis, T., Petters, M. D., Prenni, A. J.,
Roldin, P., Rose, D., Schneider, J., Su, H., Zorn, S. R., Artaxo, P., and
Andreae, M. O.: Rainforest aerosols as biogenic nuclei of clouds and
precipitation in the Amazon, Science, 329, 1513–1516, 2010. a, b
Pye, H. O., Pinder, R. W., Piletic, I. R., Xie, Y., Capps, S. L., Lin, Y.-H.,
Surratt, J. D., Zhang, Z., Gold, A., Luecken, D. J., Hutzell, W. T., Jaoui,
M., Offenberg, J. H., Kleindienst, T. E., Lewandowski, M., and Edney, E. O.:
Epoxide pathways improve model predictions of isoprene markers and reveal key
role of acidity in aerosol formation, Environ. Sci. Technol., 47,
11056–11064, 2013. a, b, c
Riedel, T. P., Lin, Y.-H., Budisulistiorini, S. H., Gaston, C. J., Thornton,
J. A., Zhang, Z., Vizuete, W., Gold, A., and Surratt, J. D.: Heterogeneous
reactions of isoprene-derived epoxides: reaction probabilities and molar
secondary organic aerosol yield estimates, Environ. Sci. Tech. Let., 2,
38–42, 2015. a
Riva, M., Budisulistiorini, S. H., Chen, Y., Zhang, Z., DAmbro, E. L., Zhang,
X., Gold, A., Turpin, B. J., Thornton, J. A., Canagaratna, M. R., and Surrat
J. D.: Chemical characterization of secondary organic aerosol from oxidation
of isoprene hydroxyhydroperoxides, Environ. Sci. Technol., 50, 9889–9899,
2016. a
Saarikoski, S., Carbone, S., Decesari, S., Giulianelli, L., Angelini, F.,
Canagaratna, M., Ng, N. L., Trimborn, A., Facchini, M. C., Fuzzi, S.,
Hillamo, R., and Worsnop, D.: Chemical characterization of springtime
submicrometer aerosol in Po Valley, Italy, Atmos. Chem. Phys., 12,
8401–8421, https://doi.org/10.5194/acp-12-8401-2012, 2012. a, b
Schultz, M. G., Stadtler, S., Schröder, S., Taraborrelli, D., Franco, B.,
Krefting, J., Henrot, A., Ferrachat, S., Lohmann, U., Neubauer, D.,
Siegenthaler-Le Drian, C., Wahl, S., Kokkola, H., Kühn, T., Rast, S.,
Schmidt, H., Stier, P., Kinnison, D., Tyndall, G. S., Orlando, J. J., and
Wespes, C.: The chemistry–climate model ECHAM6.3-HAM2.3-MOZ1.0, Geosci.
Model Dev., 11, 1695–1723, https://doi.org/10.5194/gmd-11-1695-2018, 2018. a, b, c, d, e, f, g, h
Schwartz, S. E.: Mass-transport considerations pertinent to aqueous phase
reactions of gases in liquid-water clouds, in: Chemistry of multiphase
atmospheric systems, Springer, 415–471, 1986. a
Seinfeld, J. H. and Pankow, J. F.: Organic atmospheric particulate material,
Annu. Rev. Phys. Chem., 54, 121–140, 2003. a
Shiraiwa, M., Li, Y., Tsimpidi, A. P., Karydis, V. A., Berkemeier, T.,
Pandis, S. N., Lelieveld, J., Koop, T., and Pöschl, U.: Global
distribution of particle phase state in atmospheric secondary organic
aerosols, Nat. Commun., 8, 15002, https://doi.org/10.1038/ncomms15002, 2017. a
Stadtler, S., Simpson, D., Schröder, S., Taraborrelli, D., Bott, A., and
Schultz, M.: Ozone impacts of gas–aerosol uptake in global chemistry
transport models, Atmos. Chem. Phys., 18, 3147–3171,
https://doi.org/10.5194/acp-18-3147-2018, 2018a. a
Stadtler, S., Kühn, T., Schröder, S., Taraborrelli, D., Schultz, M.
G., and Kokkola, H.: Results from Isoprene derived secondary organic aerosol
in the global aerosol-chemistry-climate model ECHAM6.3.0-HAM2.3-MOZ1.0
https://doi.org/10.23728/b2share.426c8d3200a54193886fed954b6097c2, 2018b. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I.,
Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler T., and Roeckner,
E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv.
Model Earth, Sy., 5, 146–172, 2013. a
Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E., Wilson, J.,
Ganzeveld, L., Tegen, I., Werner, M., Balkanski, Y., Schulz, M., Boucher, O.,
Minikin, A., and Petzold, A.: The aerosol-climate model ECHAM5-HAM, Atmos.
Chem. Phys., 5, 1125–1156, https://doi.org/10.5194/acp-5-1125-2005, 2005. a
Surratt, J. D., Murphy, S. M., Kroll, J. H., Ng, N. L., Hildebrandt, L.,
Sorooshian, A., Szmigielski, R., Vermeylen, R., Maenhaut, W., Claeys, M.,
Flagan, R. C., and Seinfeld, J. H.: Chemical composition of secondary organic
aerosol formed from the photooxidation of isoprene, J. Phys. Chem. A, 110,
9665–9690, 2006. a, b
Taraborrelli, D., Lawrence, M. G., Butler, T. M., Sander, R., and Lelieveld,
J.: Mainz Isoprene Mechanism 2 (MIM2): an isoprene oxidation mechanism for
regional and global atmospheric modelling, Atmos. Chem. Phys., 9, 2751–2777,
https://doi.org/10.5194/acp-9-2751-2009, 2009. a, b
Timonen, H., Aurela, M., Carbone, S., Saarnio, K., Saarikoski, S.,
Mäkelä, T., Kulmala, M., Kerminen, V.-M., Worsnop, D. R., and
Hillamo, R.: High time-resolution chemical characterization of the
water-soluble fraction of ambient aerosols with PILS-TOC-IC and AMS, Atmos.
Meas. Tech., 3, 1063–1074, https://doi.org/10.5194/amt-3-1063-2010, 2010. a
Topping, D., Barley, M., Bane, M. K., Higham, N., Aumont, B., Dingle, N., and
McFiggans, G.: UManSysProp v1.0: an online and open-source facility for
molecular property prediction and atmospheric aerosol calculations, Geosci.
Model Dev., 9, 899–914, https://doi.org/10.5194/gmd-9-899-2016, 2016. a
Tsigaridis, K. and Kanakidou, M.: Global modelling of secondary organic
aerosol in the troposphere: a sensitivity analysis, Atmos. Chem. Phys., 3,
1849–1869, https://doi.org/10.5194/acp-3-1849-2003, 2003. a, b
Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P.,
Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A.,
Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin,
M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic,
A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W.,
Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H., Lin, G., Liu, X., Luo,
G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J.-J., Müller,
J.-F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'Donnell, D., Penner, J.
E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J.,
Seland, Ø., Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D.,
Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost,
H., van Noije, T., van Zyl, P. G., von Salzen, K., Yu, F., Wang, Z., Wang,
Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The
AeroCom evaluation and intercomparison of organic aerosol in global models,
Atmos. Chem. Phys., 14, 10845–10895,
https://doi.org/10.5194/acp-14-10845-2014, 2014. a, b, c
Volkamer, R., Jimenez, J. L., San Martini, F., Dzepina, K., Zhang, Q.,
Salcedo, D., Molina, L. T., Worsnop, D. R., and Molina, M. J.: Secondary
organic aerosol formation from anthropogenic air pollution: Rapid and higher
than expected, Geophys. Res. Lett., 33, L17811, https://doi.org/10.1029/2006GL026899,
2006. a
Volkamer, R., San Martini, F., Molina, L. T., Salcedo, D., Jimenez, J. L.,
and Molina, M. J.: A missing sink for gas-phase glyoxal in Mexico City:
Formation of secondary organic aerosol, Geophys. Res. Lett., 34, L19807,
https://doi.org/10.1029/2007GL030752, 2007. a, b
Washenfelder, R., Young, C., Brown, S., Angevine, W., Atlas, E., Blake, D.,
Bon, D., Cubison, M., De Gouw, J., Dusanter, S., Flynn, J., Gilman, J. B.,
Graus, M., Griffith, S., Grossberg, N., Hayes, P. L., Jimenez, J. L., Kuster,
W. C., Lefer, B. L., Pollack, I. B., Ryerson, T. B., Stark, H., Stevens, P.
S., and Trainer, M. K.: The glyoxal budget and its contribution to organic
aerosol for Los Angeles, California, during CalNex 2010, J. Geophys.
Res.-Atmos., 116, D00V02, https://doi.org/10.1029/2011JD016314, 2011. a, b
Waxman, E. M., Dzepina, K., Ervens, B., Lee-Taylor, J., Aumont, B., Jimenez,
J. L., Madronich, S., and Volkamer, R.: Secondary organic aerosol formation
from semi-and intermediate-volatility organic compounds and glyoxal:
Relevance of O ∕ C as a tracer for aqueous multiphase chemistry, Geophys.
Res. Lett., 40, 978–982, 2013. a
Woo, J. L. and McNeill, V. F.: simpleGAMMA v1.0 – a reduced model of
secondary organic aerosol formation in the aqueous aerosol phase (aaSOA),
Geosci. Model Dev., 8, 1821–1829, https://doi.org/10.5194/gmd-8-1821-2015,
2015. a
Xu, L., Guo, H., Boyd, C. M., Klein, M., Bougiatioti, A., Cerully, K. M.,
Hite, J. R., Isaacman-VanWertz, G., Kreisberg, N. M., Knote, C., Olson, K.,
Koss, A., Goldstein, A. H., Hering, S. V., de Gouw, J., Baumann, K., Lee,
S.-H., Nenes, A., Weber, R. J., and Ng, N. L.: Effects of anthropogenic
emissions on aerosol formation from isoprene and monoterpenes in the
southeastern United States, P. Natl. Acad. Sci. USA, 112, 37–42, 2015. a, b
Zhang, Q., Jimenez, J., Canagaratna, M., et al.: Ubiquity and dominance of
oxygenated species in organic aerosols in anthropogenically-influenced
Northern Hemisphere midlatitudes, Geophys. Res. Lett., 34, L13801,
https://doi.org/10.1029/2007GL029979, 2007. a, b, c
Zhang, Q., Parworth, C., Lechner, M., and Jimenez, J.: Aerosol Mass
Spectrometer Global Database, https://doi.org/10.6084/m9.figshare.3486719, last access:
22 September 2017. a
Short summary
Atmospheric aerosols interact with our climate system and have adverse health effects. Nevertheless, these particles are a source of uncertainty in climate projections and the formation process of secondary aerosols formed by organic gas-phase precursors is particularly not fully understood. In order to gain a deeper understanding of secondary organic aerosol formation, this model system explicitly represents gas-phase and aerosol formation processes. Finally, this allows for process discussion.
Atmospheric aerosols interact with our climate system and have adverse health effects....